[ { "path": ".gitignore", "content": "### Python ###\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\nexperiments/\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# IPython Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# dotenv\n.env\n\n# virtualenv\nvenv/\nENV/\n\n# Spyder project settings\n.spyderproject\n\n# Rope project settings\n.ropeproject\n\n\n### IPythonNotebook ###\n# Temporary data\n.ipynb_checkpoints/" }, { "path": "DP/Gamblers Problem Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"### This is Example 4.3. Gambler’s Problem from Sutton's book.\\n\",\n \"\\n\",\n \"A gambler has the opportunity to make bets on the outcomes of a sequence of coin flips. \\n\",\n \"If the coin comes up heads, he wins as many dollars as he has staked on that flip; \\n\",\n \"if it is tails, he loses his stake. The game ends when the gambler wins by reaching his goal of $100, \\n\",\n \"or loses by running out of money. \\n\",\n \"\\n\",\n \"On each flip, the gambler must decide what portion of his capital to stake, in integer numbers of dollars. \\n\",\n \"This problem can be formulated as an undiscounted, episodic, finite MDP. \\n\",\n \"\\n\",\n \"The state is the gambler’s capital, s ∈ {1, 2, . . . , 99}.\\n\",\n \"The actions are stakes, a ∈ {0, 1, . . . , min(s, 100 − s)}. \\n\",\n \"The reward is zero on all transitions except those on which the gambler reaches his goal, when it is +1.\\n\",\n \"\\n\",\n \"The state-value function then gives the probability of winning from each state. A policy is a mapping from levels of capital to stakes. The optimal policy maximizes the probability of reaching the goal. Let p_h denote the probability of the coin coming up heads. If p_h is known, then the entire problem is known and it can be solved, for instance, by value iteration.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"\\n\",\n \"### Exercise 4.9 (programming)\\n\",\n \"\\n\",\n \"Implement value iteration for the gambler’s problem and solve it for p_h = 0.25 and p_h = 0.55.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def value_iteration_for_gamblers(p_h, theta=0.0001, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Args:\\n\",\n \" p_h: Probability of the coin coming up heads\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # The reward is zero on all transitions except those on which the gambler reaches his goal,\\n\",\n \" # when it is +1.\\n\",\n \" rewards = np.zeros(101)\\n\",\n \" rewards[100] = 1 \\n\",\n \" \\n\",\n \" # We introduce two dummy states corresponding to termination with capital of 0 and 100\\n\",\n \" V = np.zeros(101)\\n\",\n \" \\n\",\n \" def one_step_lookahead(s, V, rewards):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Helper function to calculate the value for all action in a given state.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" s: The gambler’s capital. Integer.\\n\",\n \" V: The vector that contains values at each state. \\n\",\n \" rewards: The reward vector.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A vector containing the expected value of each action. \\n\",\n \" Its length equals to the number of actions.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" A = np.zeros(101)\\n\",\n \" stakes = range(1, min(s, 100-s)+1) # Your minimum bet is 1, maximum bet is min(s, 100-s).\\n\",\n \" for a in stakes:\\n\",\n \" # rewards[s+a], rewards[s-a] are immediate rewards.\\n\",\n \" # V[s+a], V[s-a] are values of the next states.\\n\",\n \" # This is the core of the Bellman equation: The expected value of your action is \\n\",\n \" # the sum of immediate rewards and the value of the next state.\\n\",\n \" A[a] = p_h * (rewards[s+a] + V[s+a]*discount_factor) + (1-p_h) * (rewards[s-a] + V[s-a]*discount_factor)\\n\",\n \" return A\\n\",\n \" \\n\",\n \" while True:\\n\",\n \" # Stopping condition\\n\",\n \" delta = 0\\n\",\n \" # Update each state...\\n\",\n \" for s in range(1, 100):\\n\",\n \" # Do a one-step lookahead to find the best action\\n\",\n \" A = one_step_lookahead(s, V, rewards)\\n\",\n \" # print(s,A,V) # if you want to debug.\\n\",\n \" best_action_value = np.max(A)\\n\",\n \" # Calculate delta across all states seen so far\\n\",\n \" delta = max(delta, np.abs(best_action_value - V[s]))\\n\",\n \" # Update the value function. Ref: Sutton book eq. 4.10. \\n\",\n \" V[s] = best_action_value \\n\",\n \" # Check if we can stop \\n\",\n \" if delta < theta:\\n\",\n \" break\\n\",\n \" \\n\",\n \" # Create a deterministic policy using the optimal value function\\n\",\n \" policy = np.zeros(100)\\n\",\n \" for s in range(1, 100):\\n\",\n \" # One step lookahead to find the best action for this state\\n\",\n \" A = one_step_lookahead(s, V, rewards)\\n\",\n \" best_action = np.argmax(A)\\n\",\n \" # Always take the best action\\n\",\n \" policy[s] = best_action\\n\",\n \" \\n\",\n \" return policy, V\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Optimized Policy:\\n\",\n \"[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 12. 11. 15. 16. 17.\\n\",\n \" 18. 6. 20. 21. 3. 23. 24. 25. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.\\n\",\n \" 11. 12. 38. 11. 10. 9. 42. 7. 44. 5. 46. 47. 48. 49. 50. 1. 2. 3.\\n\",\n \" 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 11. 10. 9. 17. 7. 19. 5. 21.\\n\",\n \" 22. 23. 24. 25. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 12. 11.\\n\",\n \" 10. 9. 8. 7. 6. 5. 4. 3. 2. 1.]\\n\",\n \"\\n\",\n \"Optimized Value Function:\\n\",\n \"[0.00000000e+00 7.24792480e-05 2.89916992e-04 6.95257448e-04\\n\",\n \" 1.16010383e-03 1.76906586e-03 2.78102979e-03 4.03504074e-03\\n\",\n \" 4.66214120e-03 5.59997559e-03 7.08471239e-03 9.03964043e-03\\n\",\n \" 1.11241192e-02 1.56793594e-02 1.61464431e-02 1.69517994e-02\\n\",\n \" 1.86512806e-02 1.98249817e-02 2.24047303e-02 2.73845196e-02\\n\",\n \" 2.83388495e-02 3.04937363e-02 3.61633897e-02 3.84953022e-02\\n\",\n \" 4.44964767e-02 6.25000000e-02 6.27174377e-02 6.33700779e-02\\n\",\n \" 6.45857723e-02 6.59966059e-02 6.78135343e-02 7.08430894e-02\\n\",\n \" 7.46098323e-02 7.64884604e-02 7.93035477e-02 8.37541372e-02\\n\",\n \" 8.96225423e-02 9.58723575e-02 1.09538078e-01 1.10939329e-01\\n\",\n \" 1.13360151e-01 1.18457374e-01 1.21977661e-01 1.29716907e-01\\n\",\n \" 1.44653559e-01 1.47520113e-01 1.53983246e-01 1.70990169e-01\\n\",\n \" 1.77987434e-01 1.95990576e-01 2.50000000e-01 2.50217438e-01\\n\",\n \" 2.50870078e-01 2.52085772e-01 2.53496606e-01 2.55313534e-01\\n\",\n \" 2.58343089e-01 2.62109832e-01 2.63988460e-01 2.66803548e-01\\n\",\n \" 2.71254137e-01 2.77122542e-01 2.83372357e-01 2.97038078e-01\\n\",\n \" 2.98439329e-01 3.00860151e-01 3.05957374e-01 3.09477661e-01\\n\",\n \" 3.17216907e-01 3.32153559e-01 3.35020113e-01 3.41483246e-01\\n\",\n \" 3.58490169e-01 3.65487434e-01 3.83490576e-01 4.37500000e-01\\n\",\n \" 4.38152558e-01 4.40122454e-01 4.43757317e-01 4.47991345e-01\\n\",\n \" 4.53440603e-01 4.62529268e-01 4.73829497e-01 4.79468031e-01\\n\",\n \" 4.87912680e-01 5.01265085e-01 5.18867627e-01 5.37617932e-01\\n\",\n \" 5.78614419e-01 5.82817988e-01 5.90080452e-01 6.05372123e-01\\n\",\n \" 6.15934510e-01 6.39150720e-01 6.83960814e-01 6.92560339e-01\\n\",\n \" 7.11950883e-01 7.62970611e-01 7.83963162e-01 8.37972371e-01\\n\",\n \" 0.00000000e+00]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"policy, v = value_iteration_for_gamblers(0.25)\\n\",\n \"\\n\",\n \"print(\\\"Optimized Policy:\\\")\\n\",\n \"print(policy)\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Optimized Value Function:\\\")\\n\",\n \"print(v)\\n\",\n \"print(\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Show your results graphically, as in Figure 4.3.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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2TjDiprGhLedQRKCiIiCdcynqCWgoiIvLW6jAn5WQzNSU90KEoKIiKJ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\\nAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plotting Final Policy (action stake) vs State (Capital)\\n\",\n \"\\n\",\n \"# x axis values\\n\",\n \"x = range(100)\\n\",\n \"# corresponding y axis values\\n\",\n \"y = v[:100]\\n\",\n \" \\n\",\n \"# plotting the points \\n\",\n \"plt.plot(x, y)\\n\",\n \" \\n\",\n \"# naming the x axis\\n\",\n \"plt.xlabel('Capital')\\n\",\n \"# naming the y axis\\n\",\n \"plt.ylabel('Value Estimates')\\n\",\n \" \\n\",\n \"# giving a title to the graph\\n\",\n \"plt.title('Final Policy (action stake) vs State (Capital)')\\n\",\n \" \\n\",\n \"# function to show the plot\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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aDAmMzMrER5EsHY1HsoAGm84nHFhWRmZmXKkwhelvTqgACS3kqdMQTM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\\nRK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plotting Capital vs Final Policy\\n\",\n \"\\n\",\n \"# x axis values\\n\",\n \"x = range(100)\\n\",\n \"# corresponding y axis values\\n\",\n \"y = policy\\n\",\n \" \\n\",\n \"# plotting the bars\\n\",\n \"plt.bar(x, y, align='center', alpha=0.5)\\n\",\n \" \\n\",\n \"# naming the x axis\\n\",\n \"plt.xlabel('Capital')\\n\",\n \"# naming the y axis\\n\",\n \"plt.ylabel('Final policy (stake)')\\n\",\n \" \\n\",\n \"# giving a title to the graph\\n\",\n \"plt.title('Capital vs Final Policy')\\n\",\n \" \\n\",\n \"# function to show the plot\\n\",\n \"plt.show()\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "DP/Gamblers Problem.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"### This is Example 4.3. Gambler’s Problem from Sutton's book.\\n\",\n \"\\n\",\n \"A gambler has the opportunity to make bets on the outcomes of a sequence of coin flips. \\n\",\n \"If the coin comes up heads, he wins as many dollars as he has staked on that flip; \\n\",\n \"if it is tails, he loses his stake. The game ends when the gambler wins by reaching his goal of $100, \\n\",\n \"or loses by running out of money. \\n\",\n \"\\n\",\n \"On each flip, the gambler must decide what portion of his capital to stake, in integer numbers of dollars. \\n\",\n \"This problem can be formulated as an undiscounted, episodic, finite MDP. \\n\",\n \"\\n\",\n \"The state is the gambler’s capital, s ∈ {1, 2, . . . , 99}.\\n\",\n \"The actions are stakes, a ∈ {0, 1, . . . , min(s, 100 − s)}. \\n\",\n \"The reward is zero on all transitions except those on which the gambler reaches his goal, when it is +1.\\n\",\n \"\\n\",\n \"The state-value function then gives the probability of winning from each state. A policy is a mapping from levels of capital to stakes. The optimal policy maximizes the probability of reaching the goal. Let p_h denote the probability of the coin coming up heads. If p_h is known, then the entire problem is known and it can be solved, for instance, by value iteration.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"\\n\",\n \"### Exercise 4.9 (programming)\\n\",\n \"\\n\",\n \"Implement value iteration for the gambler’s problem and solve it for p_h = 0.25 and p_h = 0.55.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def value_iteration_for_gamblers(p_h, theta=0.0001, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Args:\\n\",\n \" p_h: Probability of the coin coming up heads\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def one_step_lookahead(s, V, rewards):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Helper function to calculate the value for all action in a given state.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" s: The gambler’s capital. Integer.\\n\",\n \" V: The vector that contains values at each state. \\n\",\n \" rewards: The reward vector.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A vector containing the expected value of each action. \\n\",\n \" Its length equals to the number of actions.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # Implement!\\n\",\n \" \\n\",\n \" return A\\n\",\n \" \\n\",\n \" # Implement!\\n\",\n \" \\n\",\n \" return policy, V\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"policy, v = value_iteration_for_gamblers(0.25)\\n\",\n \"\\n\",\n \"print(\\\"Optimized Policy:\\\")\\n\",\n \"print(policy)\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Optimized Value Function:\\\")\\n\",\n \"print(v)\\n\",\n \"print(\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Plotting Final Policy (action stake) vs State (Capital)\\n\",\n \"\\n\",\n \"# Implement!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Plotting Capital vs Final Policy\\n\",\n \"\\n\",\n \"# Implement!\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "DP/Policy Evaluation Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from IPython.core.debugger import set_trace\\n\",\n \"import numpy as np\\n\",\n \"import pprint\\n\",\n \"import sys\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.gridworld import GridworldEnv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pp = pprint.PrettyPrinter(indent=2)\\n\",\n \"env = GridworldEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Evaluate a policy given an environment and a full description of the environment's dynamics.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" policy: [S, A] shaped matrix representing the policy.\\n\",\n \" env: OpenAI env. env.P represents the transition probabilities of the environment.\\n\",\n \" env.P[s][a] is a list of transition tuples (prob, next_state, reward, done).\\n\",\n \" env.nS is a number of states in the environment. \\n\",\n \" env.nA is a number of actions in the environment.\\n\",\n \" theta: We stop evaluation once our value function change is less than theta for all states.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" Vector of length env.nS representing the value function.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # Start with a random (all 0) value function\\n\",\n \" V = np.zeros(env.nS)\\n\",\n \" while True:\\n\",\n \" delta = 0\\n\",\n \" # For each state, perform a \\\"full backup\\\"\\n\",\n \" for s in range(env.nS):\\n\",\n \" v = 0\\n\",\n \" # Look at the possible next actions\\n\",\n \" for a, action_prob in enumerate(policy[s]):\\n\",\n \" # For each action, look at the possible next states...\\n\",\n \" for prob, next_state, reward, done in env.P[s][a]:\\n\",\n \" # Calculate the expected value. Ref: Sutton book eq. 4.6.\\n\",\n \" v += action_prob * prob * (reward + discount_factor * V[next_state])\\n\",\n \" # How much our value function changed (across any states)\\n\",\n \" delta = max(delta, np.abs(v - V[s]))\\n\",\n \" V[s] = v\\n\",\n \" # Stop evaluating once our value function change is below a threshold\\n\",\n \" if delta < theta:\\n\",\n \" break\\n\",\n \" return np.array(V)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"random_policy = np.ones([env.nS, env.nA]) / env.nA\\n\",\n \"v = policy_eval(random_policy, env)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Value Function:\\n\",\n \"[ 0. -13.99993529 -19.99990698 -21.99989761 -13.99993529\\n\",\n \" -17.9999206 -19.99991379 -19.99991477 -19.99990698 -19.99991379\\n\",\n \" -17.99992725 -13.99994569 -21.99989761 -19.99991477 -13.99994569\\n\",\n \" 0. ]\\n\",\n \"\\n\",\n \"Reshaped Grid Value Function:\\n\",\n \"[[ 0. -13.99993529 -19.99990698 -21.99989761]\\n\",\n \" [-13.99993529 -17.9999206 -19.99991379 -19.99991477]\\n\",\n \" [-19.99990698 -19.99991379 -17.99992725 -13.99994569]\\n\",\n \" [-21.99989761 -19.99991477 -13.99994569 0. ]]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Value Function:\\\")\\n\",\n \"print(v)\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Reshaped Grid Value Function:\\\")\\n\",\n \"print(v.reshape(env.shape))\\n\",\n \"print(\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Test: Make sure the evaluated policy is what we expected\\n\",\n \"expected_v = np.array([0, -14, -20, -22, -14, -18, -20, -20, -20, -20, -18, -14, -22, -20, -14, 0])\\n\",\n \"np.testing.assert_array_almost_equal(v, expected_v, decimal=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "DP/Policy Evaluation.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.gridworld import GridworldEnv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"env = GridworldEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Evaluate a policy given an environment and a full description of the environment's dynamics.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" policy: [S, A] shaped matrix representing the policy.\\n\",\n \" env: OpenAI env. env.P represents the transition probabilities of the environment.\\n\",\n \" env.P[s][a] is a list of transition tuples (prob, next_state, reward, done).\\n\",\n \" env.nS is a number of states in the environment. \\n\",\n \" env.nA is a number of actions in the environment.\\n\",\n \" theta: We stop evaluation once our value function change is less than theta for all states.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" Vector of length env.nS representing the value function.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # Start with a random (all 0) value function\\n\",\n \" V = np.zeros(env.nS)\\n\",\n \" while True:\\n\",\n \" # TODO: Implement!\\n\",\n \" break\\n\",\n \" return np.array(V)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"random_policy = np.ones([env.nS, env.nA]) / env.nA\\n\",\n \"v = policy_eval(random_policy, env)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AssertionError\",\n \"evalue\": \"\\nArrays are not almost equal to 2 decimals\\n\\n(mismatch 87.5%)\\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n 0., 0., 0.])\\n y: array([ 0, -14, -20, -22, -14, -18, -20, -20, -20, -20, -18, -14, -22,\\n -20, -14, 0])\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mAssertionError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# Test: Make sure the evaluated policy is what we expected\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 2\\u001b[0m \\u001b[0mexpected_v\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0marray\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m14\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m20\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m22\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m14\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m18\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m20\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m20\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m20\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m20\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m18\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m14\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m22\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m20\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m14\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 3\\u001b[0;31m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtesting\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0massert_array_almost_equal\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mv\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mexpected_v\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdecimal\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\\u001b[0m in \\u001b[0;36massert_array_almost_equal\\u001b[0;34m(x, y, decimal, err_msg, verbose)\\u001b[0m\\n\\u001b[1;32m 914\\u001b[0m assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,\\n\\u001b[1;32m 915\\u001b[0m \\u001b[0mheader\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Arrays are not almost equal to %d decimals'\\u001b[0m \\u001b[0;34m%\\u001b[0m \\u001b[0mdecimal\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 916\\u001b[0;31m precision=decimal)\\n\\u001b[0m\\u001b[1;32m 917\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 918\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\\u001b[0m in \\u001b[0;36massert_array_compare\\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision)\\u001b[0m\\n\\u001b[1;32m 735\\u001b[0m names=('x', 'y'), precision=precision)\\n\\u001b[1;32m 736\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mcond\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 737\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0mAssertionError\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mmsg\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 738\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0mValueError\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 739\\u001b[0m \\u001b[0;32mimport\\u001b[0m \\u001b[0mtraceback\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mAssertionError\\u001b[0m: \\nArrays are not almost equal to 2 decimals\\n\\n(mismatch 87.5%)\\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n 0., 0., 0.])\\n y: array([ 0, -14, -20, -22, -14, -18, -20, -20, -20, -20, -18, -14, -22,\\n -20, -14, 0])\"\n ]\n }\n ],\n \"source\": [\n \"# Test: Make sure the evaluated policy is what we expected\\n\",\n \"expected_v = np.array([0, -14, -20, -22, -14, -18, -20, -20, -20, -20, -18, -14, -22, -20, -14, 0])\\n\",\n \"np.testing.assert_array_almost_equal(v, expected_v, decimal=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "DP/Policy Iteration Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pprint\\n\",\n \"import sys\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.gridworld import GridworldEnv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pp = pprint.PrettyPrinter(indent=2)\\n\",\n \"env = GridworldEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Taken from Policy Evaluation Exercise!\\n\",\n \"\\n\",\n \"def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Evaluate a policy given an environment and a full description of the environment's dynamics.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" policy: [S, A] shaped matrix representing the policy.\\n\",\n \" env: OpenAI env. env.P represents the transition probabilities of the environment.\\n\",\n \" env.P[s][a] is a list of transition tuples (prob, next_state, reward, done).\\n\",\n \" env.nS is a number of states in the environment. \\n\",\n \" env.nA is a number of actions in the environment.\\n\",\n \" theta: We stop evaluation once our value function change is less than theta for all states.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" Vector of length env.nS representing the value function.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # Start with a random (all 0) value function\\n\",\n \" V = np.zeros(env.nS)\\n\",\n \" while True:\\n\",\n \" delta = 0\\n\",\n \" # For each state, perform a \\\"full backup\\\"\\n\",\n \" for s in range(env.nS):\\n\",\n \" v = 0\\n\",\n \" # Look at the possible next actions\\n\",\n \" for a, action_prob in enumerate(policy[s]):\\n\",\n \" # For each action, look at the possible next states...\\n\",\n \" for prob, next_state, reward, done in env.P[s][a]:\\n\",\n \" # Calculate the expected value\\n\",\n \" v += action_prob * prob * (reward + discount_factor * V[next_state])\\n\",\n \" # How much our value function changed (across any states)\\n\",\n \" delta = max(delta, np.abs(v - V[s]))\\n\",\n \" V[s] = v\\n\",\n \" # Stop evaluating once our value function change is below a threshold\\n\",\n \" if delta < theta:\\n\",\n \" break\\n\",\n \" return np.array(V)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def policy_improvement(env, policy_eval_fn=policy_eval, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Policy Improvement Algorithm. Iteratively evaluates and improves a policy\\n\",\n \" until an optimal policy is found.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: The OpenAI environment.\\n\",\n \" policy_eval_fn: Policy Evaluation function that takes 3 arguments:\\n\",\n \" policy, env, discount_factor.\\n\",\n \" discount_factor: gamma discount factor.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A tuple (policy, V). \\n\",\n \" policy is the optimal policy, a matrix of shape [S, A] where each state s\\n\",\n \" contains a valid probability distribution over actions.\\n\",\n \" V is the value function for the optimal policy.\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" def one_step_lookahead(state, V):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Helper function to calculate the value for all action in a given state.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" state: The state to consider (int)\\n\",\n \" V: The value to use as an estimator, Vector of length env.nS\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A vector of length env.nA containing the expected value of each action.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" A = np.zeros(env.nA)\\n\",\n \" for a in range(env.nA):\\n\",\n \" for prob, next_state, reward, done in env.P[state][a]:\\n\",\n \" A[a] += prob * (reward + discount_factor * V[next_state])\\n\",\n \" return A\\n\",\n \" \\n\",\n \" # Start with a random policy\\n\",\n \" policy = np.ones([env.nS, env.nA]) / env.nA\\n\",\n \" \\n\",\n \" while True:\\n\",\n \" # Evaluate the current policy\\n\",\n \" V = policy_eval_fn(policy, env, discount_factor)\\n\",\n \" \\n\",\n \" # Will be set to false if we make any changes to the policy\\n\",\n \" policy_stable = True\\n\",\n \" \\n\",\n \" # For each state...\\n\",\n \" for s in range(env.nS):\\n\",\n \" # The best action we would take under the current policy\\n\",\n \" chosen_a = np.argmax(policy[s])\\n\",\n \" \\n\",\n \" # Find the best action by one-step lookahead\\n\",\n \" # Ties are resolved arbitarily\\n\",\n \" action_values = one_step_lookahead(s, V)\\n\",\n \" best_a = np.argmax(action_values)\\n\",\n \" \\n\",\n \" # Greedily update the policy\\n\",\n \" if chosen_a != best_a:\\n\",\n \" policy_stable = False\\n\",\n \" policy[s] = np.eye(env.nA)[best_a]\\n\",\n \" \\n\",\n \" # If the policy is stable we've found an optimal policy. Return it\\n\",\n \" if policy_stable:\\n\",\n \" return policy, V\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Policy Probability Distribution:\\n\",\n \"[[1. 0. 0. 0.]\\n\",\n \" [0. 0. 0. 1.]\\n\",\n \" [0. 0. 0. 1.]\\n\",\n \" [0. 0. 1. 0.]\\n\",\n \" [1. 0. 0. 0.]\\n\",\n \" [1. 0. 0. 0.]\\n\",\n \" [1. 0. 0. 0.]\\n\",\n \" [0. 0. 1. 0.]\\n\",\n \" [1. 0. 0. 0.]\\n\",\n \" [1. 0. 0. 0.]\\n\",\n \" [0. 1. 0. 0.]\\n\",\n \" [0. 0. 1. 0.]\\n\",\n \" [1. 0. 0. 0.]\\n\",\n \" [0. 1. 0. 0.]\\n\",\n \" [0. 1. 0. 0.]\\n\",\n \" [1. 0. 0. 0.]]\\n\",\n \"\\n\",\n \"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\\n\",\n \"[[0 3 3 2]\\n\",\n \" [0 0 0 2]\\n\",\n \" [0 0 1 2]\\n\",\n \" [0 1 1 0]]\\n\",\n \"\\n\",\n \"Value Function:\\n\",\n \"[ 0. -1. -2. -3. -1. -2. -3. -2. -2. -3. -2. -1. -3. -2. -1. 0.]\\n\",\n \"\\n\",\n \"Reshaped Grid Value Function:\\n\",\n \"[[ 0. -1. -2. -3.]\\n\",\n \" [-1. -2. -3. -2.]\\n\",\n \" [-2. -3. -2. -1.]\\n\",\n \" [-3. -2. -1. 0.]]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"policy, v = policy_improvement(env)\\n\",\n \"print(\\\"Policy Probability Distribution:\\\")\\n\",\n \"print(policy)\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\\\")\\n\",\n \"print(np.reshape(np.argmax(policy, axis=1), env.shape))\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Value Function:\\\")\\n\",\n \"print(v)\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Reshaped Grid Value Function:\\\")\\n\",\n \"print(v.reshape(env.shape))\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Test the value function\\n\",\n \"expected_v = np.array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])\\n\",\n \"np.testing.assert_array_almost_equal(v, expected_v, decimal=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "DP/Policy Iteration.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pprint\\n\",\n \"import sys\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.gridworld import GridworldEnv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"pp = pprint.PrettyPrinter(indent=2)\\n\",\n \"env = GridworldEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Taken from Policy Evaluation Exercise!\\n\",\n \"\\n\",\n \"def policy_eval(policy, env, discount_factor=1.0, theta=0.00001):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Evaluate a policy given an environment and a full description of the environment's dynamics.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" policy: [S, A] shaped matrix representing the policy.\\n\",\n \" env: OpenAI env. env.P represents the transition probabilities of the environment.\\n\",\n \" env.P[s][a] is a list of transition tuples (prob, next_state, reward, done).\\n\",\n \" env.nS is a number of states in the environment. \\n\",\n \" env.nA is a number of actions in the environment.\\n\",\n \" theta: We stop evaluation once our value function change is less than theta for all states.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" Vector of length env.nS representing the value function.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # Start with a random (all 0) value function\\n\",\n \" V = np.zeros(env.nS)\\n\",\n \" while True:\\n\",\n \" delta = 0\\n\",\n \" # For each state, perform a \\\"full backup\\\"\\n\",\n \" for s in range(env.nS):\\n\",\n \" v = 0\\n\",\n \" # Look at the possible next actions\\n\",\n \" for a, action_prob in enumerate(policy[s]):\\n\",\n \" # For each action, look at the possible next states...\\n\",\n \" for prob, next_state, reward, done in env.P[s][a]:\\n\",\n \" # Calculate the expected value\\n\",\n \" v += action_prob * prob * (reward + discount_factor * V[next_state])\\n\",\n \" # How much our value function changed (across any states)\\n\",\n \" delta = max(delta, np.abs(v - V[s]))\\n\",\n \" V[s] = v\\n\",\n \" # Stop evaluating once our value function change is below a threshold\\n\",\n \" if delta < theta:\\n\",\n \" break\\n\",\n \" return np.array(V)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def policy_improvement(env, policy_eval_fn=policy_eval, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Policy Improvement Algorithm. Iteratively evaluates and improves a policy\\n\",\n \" until an optimal policy is found.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: The OpenAI envrionment.\\n\",\n \" policy_eval_fn: Policy Evaluation function that takes 3 arguments:\\n\",\n \" policy, env, discount_factor.\\n\",\n \" discount_factor: gamma discount factor.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A tuple (policy, V). \\n\",\n \" policy is the optimal policy, a matrix of shape [S, A] where each state s\\n\",\n \" contains a valid probability distribution over actions.\\n\",\n \" V is the value function for the optimal policy.\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # Start with a random policy\\n\",\n \" policy = np.ones([env.nS, env.nA]) / env.nA\\n\",\n \" \\n\",\n \" while True:\\n\",\n \" # Implement this!\\n\",\n \" break\\n\",\n \" \\n\",\n \" return policy, np.zeros(env.nS)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Policy Probability Distribution:\\n\",\n \"[[ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]\\n\",\n \" [ 0.25 0.25 0.25 0.25]]\\n\",\n \"\\n\",\n \"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\\n\",\n \"[[0 0 0 0]\\n\",\n \" [0 0 0 0]\\n\",\n \" [0 0 0 0]\\n\",\n \" [0 0 0 0]]\\n\",\n \"\\n\",\n \"Value Function:\\n\",\n \"[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n \"\\n\",\n \"Reshaped Grid Value Function:\\n\",\n \"[[ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"policy, v = policy_improvement(env)\\n\",\n \"print(\\\"Policy Probability Distribution:\\\")\\n\",\n \"print(policy)\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\\\")\\n\",\n \"print(np.reshape(np.argmax(policy, axis=1), env.shape))\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Value Function:\\\")\\n\",\n \"print(v)\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Reshaped Grid Value Function:\\\")\\n\",\n \"print(v.reshape(env.shape))\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AssertionError\",\n \"evalue\": \"\\nArrays are not almost equal to 2 decimals\\n\\n(mismatch 87.5%)\\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n 0., 0., 0.])\\n y: array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mAssertionError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# Test the value function\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 2\\u001b[0m \\u001b[0mexpected_v\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0marray\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m[\\u001b[0m \\u001b[0;36m0\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m3\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m3\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m3\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m3\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 3\\u001b[0;31m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtesting\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0massert_array_almost_equal\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mv\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mexpected_v\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdecimal\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\\u001b[0m in \\u001b[0;36massert_array_almost_equal\\u001b[0;34m(x, y, decimal, err_msg, verbose)\\u001b[0m\\n\\u001b[1;32m 914\\u001b[0m assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,\\n\\u001b[1;32m 915\\u001b[0m \\u001b[0mheader\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Arrays are not almost equal to %d decimals'\\u001b[0m \\u001b[0;34m%\\u001b[0m \\u001b[0mdecimal\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 916\\u001b[0;31m precision=decimal)\\n\\u001b[0m\\u001b[1;32m 917\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 918\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\\u001b[0m in \\u001b[0;36massert_array_compare\\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision)\\u001b[0m\\n\\u001b[1;32m 735\\u001b[0m names=('x', 'y'), precision=precision)\\n\\u001b[1;32m 736\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mcond\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 737\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0mAssertionError\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mmsg\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 738\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0mValueError\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 739\\u001b[0m \\u001b[0;32mimport\\u001b[0m \\u001b[0mtraceback\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mAssertionError\\u001b[0m: \\nArrays are not almost equal to 2 decimals\\n\\n(mismatch 87.5%)\\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n 0., 0., 0.])\\n y: array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])\"\n ]\n }\n ],\n \"source\": [\n \"# Test the value function\\n\",\n \"expected_v = np.array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])\\n\",\n \"np.testing.assert_array_almost_equal(v, expected_v, decimal=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "DP/README.md", "content": "## Model-Based RL: Policy and Value Iteration using Dynamic Programming\n\n### Learning Goals\n\n- Understand the difference between Policy Evaluation and Policy Improvement and how these processes interact\n- Understand the Policy Iteration Algorithm\n- Understand the Value Iteration Algorithm\n- Understand the Limitations of Dynamic Programming Approaches\n\n\n### Summary\n\n- Dynamic Programming (DP) methods assume that we have a perfect model of the environment's Markov Decision Process (MDP). That's usually not the case in practice, but it's important to study DP anyway.\n- Policy Evaluation: Calculates the state-value function `V(s)` for a given policy. In DP this is done using a \"full backup\". At each state, we look ahead one step at each possible action and next state. We can only do this because we have a perfect model of the environment.\n- Full backups are basically the Bellman equations turned into updates.\n- Policy Improvement: Given the correct state-value function for a policy we can act greedily with respect to it (i.e. pick the best action at each state). Then we are guaranteed to improve the policy or keep it fixed if it's already optimal.\n- Policy Iteration: Iteratively perform Policy Evaluation and Policy Improvement until we reach the optimal policy.\n- Value Iteration: Instead of doing multiple steps of Policy Evaluation to find the \"correct\" V(s) we only do a single step and improve the policy immediately. In practice, this converges faster.\n- Generalized Policy Iteration: The process of iteratively doing policy evaluation and improvement. We can pick different algorithms for each of these steps but the basic idea stays the same.\n- DP methods bootstrap: They update estimates based on other estimates (one step ahead).\n\n\n### Lectures & Readings\n\n**Required:**\n\n- David Silver's RL Course Lecture 3 - Planning by Dynamic Programming ([video](https://www.youtube.com/watch?v=Nd1-UUMVfz4), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/DP.pdf))\n\n**Optional:**\n\n- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 4: Dynamic Programming\n\n\n### Exercises\n\n- Implement Policy Evaluation in Python (Gridworld)\n - [Exercise](Policy%20Evaluation.ipynb)\n - [Solution](Policy%20Evaluation%20Solution.ipynb)\n\n- Implement Policy Iteration in Python (Gridworld)\n - [Exercise](Policy%20Iteration.ipynb)\n - [Solution](Policy%20Iteration%20Solution.ipynb)\n\n- Implement Value Iteration in Python (Gridworld)\n - [Exercise](Value%20Iteration.ipynb)\n - [Solution](Value%20Iteration%20Solution.ipynb)\n\n- Implement Gambler's Problem\n - [Exercise](Gamblers%20Problem.ipynb)\n - [Solution](Gamblers%20Problem%20Solution.ipynb)" }, { "path": "DP/Value Iteration Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pprint\\n\",\n \"import sys\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.gridworld import GridworldEnv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pp = pprint.PrettyPrinter(indent=2)\\n\",\n \"env = GridworldEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def value_iteration(env, theta=0.0001, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Value Iteration Algorithm.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI env. env.P represents the transition probabilities of the environment.\\n\",\n \" env.P[s][a] is a list of transition tuples (prob, next_state, reward, done).\\n\",\n \" env.nS is a number of states in the environment. \\n\",\n \" env.nA is a number of actions in the environment.\\n\",\n \" theta: We stop evaluation once our value function change is less than theta for all states.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A tuple (policy, V) of the optimal policy and the optimal value function.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def one_step_lookahead(state, V):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Helper function to calculate the value for all action in a given state.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" state: The state to consider (int)\\n\",\n \" V: The value to use as an estimator, Vector of length env.nS\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A vector of length env.nA containing the expected value of each action.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" A = np.zeros(env.nA)\\n\",\n \" for a in range(env.nA):\\n\",\n \" for prob, next_state, reward, done in env.P[state][a]:\\n\",\n \" A[a] += prob * (reward + discount_factor * V[next_state])\\n\",\n \" return A\\n\",\n \" \\n\",\n \" V = np.zeros(env.nS)\\n\",\n \" while True:\\n\",\n \" # Stopping condition\\n\",\n \" delta = 0\\n\",\n \" # Update each state...\\n\",\n \" for s in range(env.nS):\\n\",\n \" # Do a one-step lookahead to find the best action\\n\",\n \" A = one_step_lookahead(s, V)\\n\",\n \" best_action_value = np.max(A)\\n\",\n \" # Calculate delta across all states seen so far\\n\",\n \" delta = max(delta, np.abs(best_action_value - V[s]))\\n\",\n \" # Update the value function. Ref: Sutton book eq. 4.10. \\n\",\n \" V[s] = best_action_value \\n\",\n \" # Check if we can stop \\n\",\n \" if delta < theta:\\n\",\n \" break\\n\",\n \" \\n\",\n \" # Create a deterministic policy using the optimal value function\\n\",\n \" policy = np.zeros([env.nS, env.nA])\\n\",\n \" for s in range(env.nS):\\n\",\n \" # One step lookahead to find the best action for this state\\n\",\n \" A = one_step_lookahead(s, V)\\n\",\n \" best_action = np.argmax(A)\\n\",\n \" # Always take the best action\\n\",\n \" policy[s, best_action] = 1.0\\n\",\n \" \\n\",\n \" return policy, V\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Policy Probability Distribution:\\n\",\n \"[[1. 0. 0. 0.]\\n\",\n \" [0. 0. 0. 1.]\\n\",\n \" [0. 0. 0. 1.]\\n\",\n \" [0. 0. 1. 0.]\\n\",\n \" [1. 0. 0. 0.]\\n\",\n \" [1. 0. 0. 0.]\\n\",\n \" [1. 0. 0. 0.]\\n\",\n \" [0. 0. 1. 0.]\\n\",\n \" [1. 0. 0. 0.]\\n\",\n \" [1. 0. 0. 0.]\\n\",\n \" [0. 1. 0. 0.]\\n\",\n \" [0. 0. 1. 0.]\\n\",\n \" [1. 0. 0. 0.]\\n\",\n \" [0. 1. 0. 0.]\\n\",\n \" [0. 1. 0. 0.]\\n\",\n \" [1. 0. 0. 0.]]\\n\",\n \"\\n\",\n \"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\\n\",\n \"[[0 3 3 2]\\n\",\n \" [0 0 0 2]\\n\",\n \" [0 0 1 2]\\n\",\n \" [0 1 1 0]]\\n\",\n \"\\n\",\n \"Value Function:\\n\",\n \"[ 0. -1. -2. -3. -1. -2. -3. -2. -2. -3. -2. -1. -3. -2. -1. 0.]\\n\",\n \"\\n\",\n \"Reshaped Grid Value Function:\\n\",\n \"[[ 0. -1. -2. -3.]\\n\",\n \" [-1. -2. -3. -2.]\\n\",\n \" [-2. -3. -2. -1.]\\n\",\n \" [-3. -2. -1. 0.]]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"policy, v = value_iteration(env)\\n\",\n \"\\n\",\n \"print(\\\"Policy Probability Distribution:\\\")\\n\",\n \"print(policy)\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\\\")\\n\",\n \"print(np.reshape(np.argmax(policy, axis=1), env.shape))\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Value Function:\\\")\\n\",\n \"print(v)\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Reshaped Grid Value Function:\\\")\\n\",\n \"print(v.reshape(env.shape))\\n\",\n \"print(\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Test the value function\\n\",\n \"expected_v = np.array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])\\n\",\n \"np.testing.assert_array_almost_equal(v, expected_v, decimal=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "DP/Value Iteration.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pprint\\n\",\n \"import sys\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.gridworld import GridworldEnv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"pp = pprint.PrettyPrinter(indent=2)\\n\",\n \"env = GridworldEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def value_iteration(env, theta=0.0001, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Value Iteration Algorithm.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI env. env.P represents the transition probabilities of the environment.\\n\",\n \" env.P[s][a] is a list of transition tuples (prob, next_state, reward, done).\\n\",\n \" env.nS is a number of states in the environment. \\n\",\n \" env.nA is a number of actions in the environment.\\n\",\n \" theta: We stop evaluation once our value function change is less than theta for all states.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A tuple (policy, V) of the optimal policy and the optimal value function. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \"\\n\",\n \" V = np.zeros(env.nS)\\n\",\n \" policy = np.zeros([env.nS, env.nA])\\n\",\n \" \\n\",\n \" # Implement!\\n\",\n \" return policy, V\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Policy Probability Distribution:\\n\",\n \"[[ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]]\\n\",\n \"\\n\",\n \"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\\n\",\n \"[[0 0 0 0]\\n\",\n \" [0 0 0 0]\\n\",\n \" [0 0 0 0]\\n\",\n \" [0 0 0 0]]\\n\",\n \"\\n\",\n \"Value Function:\\n\",\n \"[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\\n\",\n \"\\n\",\n \"Reshaped Grid Value Function:\\n\",\n \"[[ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]\\n\",\n \" [ 0. 0. 0. 0.]]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"policy, v = value_iteration(env)\\n\",\n \"\\n\",\n \"print(\\\"Policy Probability Distribution:\\\")\\n\",\n \"print(policy)\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\\\")\\n\",\n \"print(np.reshape(np.argmax(policy, axis=1), env.shape))\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Value Function:\\\")\\n\",\n \"print(v)\\n\",\n \"print(\\\"\\\")\\n\",\n \"\\n\",\n \"print(\\\"Reshaped Grid Value Function:\\\")\\n\",\n \"print(v.reshape(env.shape))\\n\",\n \"print(\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AssertionError\",\n \"evalue\": \"\\nArrays are not almost equal to 2 decimals\\n\\n(mismatch 87.5%)\\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n 0., 0., 0.])\\n y: array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mAssertionError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# Test the value function\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 2\\u001b[0m \\u001b[0mexpected_v\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0marray\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m[\\u001b[0m \\u001b[0;36m0\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m3\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m3\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m3\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m3\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m-\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 3\\u001b[0;31m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mtesting\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0massert_array_almost_equal\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mv\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mexpected_v\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdecimal\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m2\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\\u001b[0m in \\u001b[0;36massert_array_almost_equal\\u001b[0;34m(x, y, decimal, err_msg, verbose)\\u001b[0m\\n\\u001b[1;32m 914\\u001b[0m assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,\\n\\u001b[1;32m 915\\u001b[0m \\u001b[0mheader\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Arrays are not almost equal to %d decimals'\\u001b[0m \\u001b[0;34m%\\u001b[0m \\u001b[0mdecimal\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 916\\u001b[0;31m precision=decimal)\\n\\u001b[0m\\u001b[1;32m 917\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 918\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/Users/dennybritz/venvs/tf/lib/python3.5/site-packages/numpy/testing/utils.py\\u001b[0m in \\u001b[0;36massert_array_compare\\u001b[0;34m(comparison, x, y, err_msg, verbose, header, precision)\\u001b[0m\\n\\u001b[1;32m 735\\u001b[0m names=('x', 'y'), precision=precision)\\n\\u001b[1;32m 736\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mcond\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 737\\u001b[0;31m \\u001b[0;32mraise\\u001b[0m \\u001b[0mAssertionError\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mmsg\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 738\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0mValueError\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 739\\u001b[0m \\u001b[0;32mimport\\u001b[0m \\u001b[0mtraceback\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mAssertionError\\u001b[0m: \\nArrays are not almost equal to 2 decimals\\n\\n(mismatch 87.5%)\\n x: array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\\n 0., 0., 0.])\\n y: array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])\"\n ]\n }\n ],\n \"source\": [\n \"# Test the value function\\n\",\n \"expected_v = np.array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])\\n\",\n \"np.testing.assert_array_almost_equal(v, expected_v, decimal=2)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "DQN/.gitignore", "content": "experiments/" }, { "path": "DQN/Breakout Playground.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import numpy as np\\n\",\n \"from matplotlib import pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2016-11-16 23:36:18,386] Making new env: Breakout-v0\\n\"\n ]\n }\n ],\n \"source\": [\n \"env = gym.envs.make(\\\"Breakout-v0\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Action space size: 6\\n\",\n \"['NOOP', 'FIRE', 'RIGHT', 'LEFT', 'RIGHTFIRE', 'LEFTFIRE']\\n\",\n \"Observation space shape: (210, 160, 3)\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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X/6lUB3hTXPA94sMh7uNQ3PO0Br+qLbCPwRyXVVFfdK8ma2Y4zeKrrM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\\nGIIAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"print(\\\"Action space size: {}\\\".format(env.action_space.n))\\n\",\n \"print(env.get_action_meanings()) # env.unwrapped.get_action_meanings() for gym 0.8.0 or later\\n\",\n \"\\n\",\n \"observation = env.reset()\\n\",\n \"print(\\\"Observation space shape: {}\\\".format(observation.shape))\\n\",\n \"\\n\",\n \"plt.figure()\\n\",\n \"plt.imshow(env.render(mode='rgb_array'))\\n\",\n \"\\n\",\n \"[env.step(2) for x in range(1)]\\n\",\n \"plt.figure()\\n\",\n \"plt.imshow(env.render(mode='rgb_array'))\\n\",\n \"\\n\",\n \"env.render(close=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 73,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAQQAAAD/CAYAAAAXKqhkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nl4WOAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Check out what a cropped image looks like\\n\",\n \"plt.imshow(observation[34:-16,:,:])\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "DQN/Deep Q Learning Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"from gym.wrappers import Monitor\\n\",\n \"import itertools\\n\",\n \"import numpy as np\\n\",\n \"import os\\n\",\n \"import random\\n\",\n \"import sys\\n\",\n \"import psutil\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\")\\n\",\n \"\\n\",\n \"from lib import plotting\\n\",\n \"from collections import deque, namedtuple\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"env = gym.envs.make(\\\"Breakout-v0\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Atari Actions: 0 (noop), 1 (fire), 2 (left) and 3 (right) are valid actions\\n\",\n \"VALID_ACTIONS = [0, 1, 2, 3]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class StateProcessor():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Processes a raw Atari images. Resizes it and converts it to grayscale.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def __init__(self):\\n\",\n \" # Build the Tensorflow graph\\n\",\n \" with tf.variable_scope(\\\"state_processor\\\"):\\n\",\n \" self.input_state = tf.placeholder(shape=[210, 160, 3], dtype=tf.uint8)\\n\",\n \" self.output = tf.image.rgb_to_grayscale(self.input_state)\\n\",\n \" self.output = tf.image.crop_to_bounding_box(self.output, 34, 0, 160, 160)\\n\",\n \" self.output = tf.image.resize_images(\\n\",\n \" self.output, [84, 84], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\\n\",\n \" self.output = tf.squeeze(self.output)\\n\",\n \"\\n\",\n \" def process(self, sess, state):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Args:\\n\",\n \" sess: A Tensorflow session object\\n\",\n \" state: A [210, 160, 3] Atari RGB State\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" A processed [84, 84] state representing grayscale values.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" return sess.run(self.output, { self.input_state: state })\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class Estimator():\\n\",\n \" \\\"\\\"\\\"Q-Value Estimator neural network.\\n\",\n \"\\n\",\n \" This network is used for both the Q-Network and the Target Network.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" def __init__(self, scope=\\\"estimator\\\", summaries_dir=None):\\n\",\n \" self.scope = scope\\n\",\n \" # Writes Tensorboard summaries to disk\\n\",\n \" self.summary_writer = None\\n\",\n \" with tf.variable_scope(scope):\\n\",\n \" # Build the graph\\n\",\n \" self._build_model()\\n\",\n \" if summaries_dir:\\n\",\n \" summary_dir = os.path.join(summaries_dir, \\\"summaries_{}\\\".format(scope))\\n\",\n \" if not os.path.exists(summary_dir):\\n\",\n \" os.makedirs(summary_dir)\\n\",\n \" self.summary_writer = tf.summary.FileWriter(summary_dir)\\n\",\n \"\\n\",\n \" def _build_model(self):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Builds the Tensorflow graph.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Placeholders for our input\\n\",\n \" # Our input are 4 grayscale frames of shape 84, 84 each\\n\",\n \" self.X_pl = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.uint8, name=\\\"X\\\")\\n\",\n \" # The TD target value\\n\",\n \" self.y_pl = tf.placeholder(shape=[None], dtype=tf.float32, name=\\\"y\\\")\\n\",\n \" # Integer id of which action was selected\\n\",\n \" self.actions_pl = tf.placeholder(shape=[None], dtype=tf.int32, name=\\\"actions\\\")\\n\",\n \"\\n\",\n \" X = tf.to_float(self.X_pl) / 255.0\\n\",\n \" batch_size = tf.shape(self.X_pl)[0]\\n\",\n \"\\n\",\n \" # Three convolutional layers\\n\",\n \" conv1 = tf.contrib.layers.conv2d(\\n\",\n \" X, 32, 8, 4, activation_fn=tf.nn.relu)\\n\",\n \" conv2 = tf.contrib.layers.conv2d(\\n\",\n \" conv1, 64, 4, 2, activation_fn=tf.nn.relu)\\n\",\n \" conv3 = tf.contrib.layers.conv2d(\\n\",\n \" conv2, 64, 3, 1, activation_fn=tf.nn.relu)\\n\",\n \"\\n\",\n \" # Fully connected layers\\n\",\n \" flattened = tf.contrib.layers.flatten(conv3)\\n\",\n \" fc1 = tf.contrib.layers.fully_connected(flattened, 512)\\n\",\n \" self.predictions = tf.contrib.layers.fully_connected(fc1, len(VALID_ACTIONS))\\n\",\n \"\\n\",\n \" # Get the predictions for the chosen actions only\\n\",\n \" gather_indices = tf.range(batch_size) * tf.shape(self.predictions)[1] + self.actions_pl\\n\",\n \" self.action_predictions = tf.gather(tf.reshape(self.predictions, [-1]), gather_indices)\\n\",\n \"\\n\",\n \" # Calculate the loss\\n\",\n \" self.losses = tf.squared_difference(self.y_pl, self.action_predictions)\\n\",\n \" self.loss = tf.reduce_mean(self.losses)\\n\",\n \"\\n\",\n \" # Optimizer Parameters from original paper\\n\",\n \" self.optimizer = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6)\\n\",\n \" self.train_op = self.optimizer.minimize(self.loss, global_step=tf.contrib.framework.get_global_step())\\n\",\n \"\\n\",\n \" # Summaries for Tensorboard\\n\",\n \" self.summaries = tf.summary.merge([\\n\",\n \" tf.summary.scalar(\\\"loss\\\", self.loss),\\n\",\n \" tf.summary.histogram(\\\"loss_hist\\\", self.losses),\\n\",\n \" tf.summary.histogram(\\\"q_values_hist\\\", self.predictions),\\n\",\n \" tf.summary.scalar(\\\"max_q_value\\\", tf.reduce_max(self.predictions))\\n\",\n \" ])\\n\",\n \"\\n\",\n \" def predict(self, sess, s):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Predicts action values.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" sess: Tensorflow session\\n\",\n \" s: State input of shape [batch_size, 4, 84, 84, 1]\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" Tensor of shape [batch_size, NUM_VALID_ACTIONS] containing the estimated \\n\",\n \" action values.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" return sess.run(self.predictions, { self.X_pl: s })\\n\",\n \"\\n\",\n \" def update(self, sess, s, a, y):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Updates the estimator towards the given targets.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" sess: Tensorflow session object\\n\",\n \" s: State input of shape [batch_size, 4, 84, 84, 1]\\n\",\n \" a: Chosen actions of shape [batch_size]\\n\",\n \" y: Targets of shape [batch_size]\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" The calculated loss on the batch.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" feed_dict = { self.X_pl: s, self.y_pl: y, self.actions_pl: a }\\n\",\n \" summaries, global_step, _, loss = sess.run(\\n\",\n \" [self.summaries, tf.contrib.framework.get_global_step(), self.train_op, self.loss],\\n\",\n \" feed_dict)\\n\",\n \" if self.summary_writer:\\n\",\n \" self.summary_writer.add_summary(summaries, global_step)\\n\",\n \" return loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# For Testing....\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"global_step = tf.Variable(0, name=\\\"global_step\\\", trainable=False)\\n\",\n \"\\n\",\n \"e = Estimator(scope=\\\"test\\\")\\n\",\n \"sp = StateProcessor()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(tf.global_variables_initializer())\\n\",\n \" \\n\",\n \" # Example observation batch\\n\",\n \" observation = env.reset()\\n\",\n \" \\n\",\n \" observation_p = sp.process(sess, observation)\\n\",\n \" observation = np.stack([observation_p] * 4, axis=2)\\n\",\n \" observations = np.array([observation] * 2)\\n\",\n \" \\n\",\n \" # Test Prediction\\n\",\n \" print(e.predict(sess, observations))\\n\",\n \"\\n\",\n \" # Test training step\\n\",\n \" y = np.array([10.0, 10.0])\\n\",\n \" a = np.array([1, 3])\\n\",\n \" print(e.update(sess, observations, a, y))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class ModelParametersCopier():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Copy model parameters of one estimator to another.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def __init__(self, estimator1, estimator2):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Defines copy-work operation graph. \\n\",\n \" Args:\\n\",\n \" estimator1: Estimator to copy the paramters from\\n\",\n \" estimator2: Estimator to copy the parameters to\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" e1_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator1.scope)]\\n\",\n \" e1_params = sorted(e1_params, key=lambda v: v.name)\\n\",\n \" e2_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator2.scope)]\\n\",\n \" e2_params = sorted(e2_params, key=lambda v: v.name)\\n\",\n \"\\n\",\n \" self.update_ops = []\\n\",\n \" for e1_v, e2_v in zip(e1_params, e2_params):\\n\",\n \" op = e2_v.assign(e1_v)\\n\",\n \" self.update_ops.append(op)\\n\",\n \" \\n\",\n \" def make(self, sess):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Makes copy.\\n\",\n \" Args:\\n\",\n \" sess: Tensorflow session instance\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" sess.run(self.update_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def make_epsilon_greedy_policy(estimator, nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates an epsilon-greedy policy based on a given Q-function approximator and epsilon.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" estimator: An estimator that returns q values for a given state\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" A function that takes the (sess, observation, epsilon) as an argument and returns\\n\",\n \" the probabilities for each action in the form of a numpy array of length nA.\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def policy_fn(sess, observation, epsilon):\\n\",\n \" A = np.ones(nA, dtype=float) * epsilon / nA\\n\",\n \" q_values = estimator.predict(sess, np.expand_dims(observation, 0))[0]\\n\",\n \" best_action = np.argmax(q_values)\\n\",\n \" A[best_action] += (1.0 - epsilon)\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def deep_q_learning(sess,\\n\",\n \" env,\\n\",\n \" q_estimator,\\n\",\n \" target_estimator,\\n\",\n \" state_processor,\\n\",\n \" num_episodes,\\n\",\n \" experiment_dir,\\n\",\n \" replay_memory_size=500000,\\n\",\n \" replay_memory_init_size=50000,\\n\",\n \" update_target_estimator_every=10000,\\n\",\n \" discount_factor=0.99,\\n\",\n \" epsilon_start=1.0,\\n\",\n \" epsilon_end=0.1,\\n\",\n \" epsilon_decay_steps=500000,\\n\",\n \" batch_size=32,\\n\",\n \" record_video_every=50):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Q-Learning algorithm for off-policy TD control using Function Approximation.\\n\",\n \" Finds the optimal greedy policy while following an epsilon-greedy policy.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" sess: Tensorflow Session object\\n\",\n \" env: OpenAI environment\\n\",\n \" q_estimator: Estimator object used for the q values\\n\",\n \" target_estimator: Estimator object used for the targets\\n\",\n \" state_processor: A StateProcessor object\\n\",\n \" num_episodes: Number of episodes to run for\\n\",\n \" experiment_dir: Directory to save Tensorflow summaries in\\n\",\n \" replay_memory_size: Size of the replay memory\\n\",\n \" replay_memory_init_size: Number of random experiences to sampel when initializing \\n\",\n \" the reply memory.\\n\",\n \" update_target_estimator_every: Copy parameters from the Q estimator to the \\n\",\n \" target estimator every N steps\\n\",\n \" discount_factor: Gamma discount factor\\n\",\n \" epsilon_start: Chance to sample a random action when taking an action.\\n\",\n \" Epsilon is decayed over time and this is the start value\\n\",\n \" epsilon_end: The final minimum value of epsilon after decaying is done\\n\",\n \" epsilon_decay_steps: Number of steps to decay epsilon over\\n\",\n \" batch_size: Size of batches to sample from the replay memory\\n\",\n \" record_video_every: Record a video every N episodes\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" Transition = namedtuple(\\\"Transition\\\", [\\\"state\\\", \\\"action\\\", \\\"reward\\\", \\\"next_state\\\", \\\"done\\\"])\\n\",\n \"\\n\",\n \" # The replay memory\\n\",\n \" replay_memory = []\\n\",\n \" \\n\",\n \" # Make model copier object\\n\",\n \" estimator_copy = ModelParametersCopier(q_estimator, target_estimator)\\n\",\n \"\\n\",\n \" # Keeps track of useful statistics\\n\",\n \" stats = plotting.EpisodeStats(\\n\",\n \" episode_lengths=np.zeros(num_episodes),\\n\",\n \" episode_rewards=np.zeros(num_episodes))\\n\",\n \" \\n\",\n \" # For 'system/' summaries, usefull to check if currrent process looks healthy\\n\",\n \" current_process = psutil.Process()\\n\",\n \"\\n\",\n \" # Create directories for checkpoints and summaries\\n\",\n \" checkpoint_dir = os.path.join(experiment_dir, \\\"checkpoints\\\")\\n\",\n \" checkpoint_path = os.path.join(checkpoint_dir, \\\"model\\\")\\n\",\n \" monitor_path = os.path.join(experiment_dir, \\\"monitor\\\")\\n\",\n \" \\n\",\n \" if not os.path.exists(checkpoint_dir):\\n\",\n \" os.makedirs(checkpoint_dir)\\n\",\n \" if not os.path.exists(monitor_path):\\n\",\n \" os.makedirs(monitor_path)\\n\",\n \"\\n\",\n \" saver = tf.train.Saver()\\n\",\n \" # Load a previous checkpoint if we find one\\n\",\n \" latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)\\n\",\n \" if latest_checkpoint:\\n\",\n \" print(\\\"Loading model checkpoint {}...\\\\n\\\".format(latest_checkpoint))\\n\",\n \" saver.restore(sess, latest_checkpoint)\\n\",\n \" \\n\",\n \" # Get the current time step\\n\",\n \" total_t = sess.run(tf.contrib.framework.get_global_step())\\n\",\n \"\\n\",\n \" # The epsilon decay schedule\\n\",\n \" epsilons = np.linspace(epsilon_start, epsilon_end, epsilon_decay_steps)\\n\",\n \"\\n\",\n \" # The policy we're following\\n\",\n \" policy = make_epsilon_greedy_policy(\\n\",\n \" q_estimator,\\n\",\n \" len(VALID_ACTIONS))\\n\",\n \"\\n\",\n \" # Populate the replay memory with initial experience\\n\",\n \" print(\\\"Populating replay memory...\\\")\\n\",\n \" state = env.reset()\\n\",\n \" state = state_processor.process(sess, state)\\n\",\n \" state = np.stack([state] * 4, axis=2)\\n\",\n \" for i in range(replay_memory_init_size):\\n\",\n \" action_probs = policy(sess, state, epsilons[min(total_t, epsilon_decay_steps-1)])\\n\",\n \" action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\\n\",\n \" next_state, reward, done, _ = env.step(VALID_ACTIONS[action])\\n\",\n \" next_state = state_processor.process(sess, next_state)\\n\",\n \" next_state = np.append(state[:,:,1:], np.expand_dims(next_state, 2), axis=2)\\n\",\n \" replay_memory.append(Transition(state, action, reward, next_state, done))\\n\",\n \" if done:\\n\",\n \" state = env.reset()\\n\",\n \" state = state_processor.process(sess, state)\\n\",\n \" state = np.stack([state] * 4, axis=2)\\n\",\n \" else:\\n\",\n \" state = next_state\\n\",\n \"\\n\",\n \"\\n\",\n \" # Record videos\\n\",\n \" # Add env Monitor wrapper\\n\",\n \" env = Monitor(env, directory=monitor_path, video_callable=lambda count: count % record_video_every == 0, resume=True)\\n\",\n \"\\n\",\n \" for i_episode in range(num_episodes):\\n\",\n \"\\n\",\n \" # Save the current checkpoint\\n\",\n \" saver.save(tf.get_default_session(), checkpoint_path)\\n\",\n \"\\n\",\n \" # Reset the environment\\n\",\n \" state = env.reset()\\n\",\n \" state = state_processor.process(sess, state)\\n\",\n \" state = np.stack([state] * 4, axis=2)\\n\",\n \" loss = None\\n\",\n \"\\n\",\n \" # One step in the environment\\n\",\n \" for t in itertools.count():\\n\",\n \"\\n\",\n \" # Epsilon for this time step\\n\",\n \" epsilon = epsilons[min(total_t, epsilon_decay_steps-1)]\\n\",\n \"\\n\",\n \" # Maybe update the target estimator\\n\",\n \" if total_t % update_target_estimator_every == 0:\\n\",\n \" estimator_copy.make(sess)\\n\",\n \" print(\\\"\\\\nCopied model parameters to target network.\\\")\\n\",\n \"\\n\",\n \" # Print out which step we're on, useful for debugging.\\n\",\n \" print(\\\"\\\\rStep {} ({}) @ Episode {}/{}, loss: {}\\\".format(\\n\",\n \" t, total_t, i_episode + 1, num_episodes, loss), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \"\\n\",\n \" # Take a step\\n\",\n \" action_probs = policy(sess, state, epsilon)\\n\",\n \" action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\\n\",\n \" next_state, reward, done, _ = env.step(VALID_ACTIONS[action])\\n\",\n \" next_state = state_processor.process(sess, next_state)\\n\",\n \" next_state = np.append(state[:,:,1:], np.expand_dims(next_state, 2), axis=2)\\n\",\n \"\\n\",\n \" # If our replay memory is full, pop the first element\\n\",\n \" if len(replay_memory) == replay_memory_size:\\n\",\n \" replay_memory.pop(0)\\n\",\n \"\\n\",\n \" # Save transition to replay memory\\n\",\n \" replay_memory.append(Transition(state, action, reward, next_state, done)) \\n\",\n \"\\n\",\n \" # Update statistics\\n\",\n \" stats.episode_rewards[i_episode] += reward\\n\",\n \" stats.episode_lengths[i_episode] = t\\n\",\n \"\\n\",\n \" # Sample a minibatch from the replay memory\\n\",\n \" samples = random.sample(replay_memory, batch_size)\\n\",\n \" states_batch, action_batch, reward_batch, next_states_batch, done_batch = map(np.array, zip(*samples))\\n\",\n \"\\n\",\n \" # Calculate q values and targets\\n\",\n \" q_values_next = target_estimator.predict(sess, next_states_batch)\\n\",\n \" targets_batch = reward_batch + np.invert(done_batch).astype(np.float32) * discount_factor * np.amax(q_values_next, axis=1)\\n\",\n \"\\n\",\n \" # Perform gradient descent update\\n\",\n \" states_batch = np.array(states_batch)\\n\",\n \" loss = q_estimator.update(sess, states_batch, action_batch, targets_batch)\\n\",\n \"\\n\",\n \" if done:\\n\",\n \" break\\n\",\n \"\\n\",\n \" state = next_state\\n\",\n \" total_t += 1\\n\",\n \"\\n\",\n \" # Add summaries to tensorboard\\n\",\n \" episode_summary = tf.Summary()\\n\",\n \" episode_summary.value.add(simple_value=epsilon, tag=\\\"episode/epsilon\\\")\\n\",\n \" episode_summary.value.add(simple_value=stats.episode_rewards[i_episode], tag=\\\"episode/reward\\\")\\n\",\n \" episode_summary.value.add(simple_value=stats.episode_lengths[i_episode], tag=\\\"episode/length\\\")\\n\",\n \" episode_summary.value.add(simple_value=current_process.cpu_percent(), tag=\\\"system/cpu_usage_percent\\\")\\n\",\n \" episode_summary.value.add(simple_value=current_process.memory_percent(memtype=\\\"vms\\\"), tag=\\\"system/v_memeory_usage_percent\\\")\\n\",\n \" q_estimator.summary_writer.add_summary(episode_summary, i_episode)\\n\",\n \" q_estimator.summary_writer.flush()\\n\",\n \" \\n\",\n \" yield total_t, plotting.EpisodeStats(\\n\",\n \" episode_lengths=stats.episode_lengths[:i_episode+1],\\n\",\n \" episode_rewards=stats.episode_rewards[:i_episode+1])\\n\",\n \"\\n\",\n \" return stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"# Where we save our checkpoints and graphs\\n\",\n \"experiment_dir = os.path.abspath(\\\"./experiments/{}\\\".format(env.spec.id))\\n\",\n \"\\n\",\n \"# Create a glboal step variable\\n\",\n \"global_step = tf.Variable(0, name='global_step', trainable=False)\\n\",\n \" \\n\",\n \"# Create estimators\\n\",\n \"q_estimator = Estimator(scope=\\\"q_estimator\\\", summaries_dir=experiment_dir)\\n\",\n \"target_estimator = Estimator(scope=\\\"target_q\\\")\\n\",\n \"\\n\",\n \"# State processor\\n\",\n \"state_processor = StateProcessor()\\n\",\n \"\\n\",\n \"# Run it!\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(tf.global_variables_initializer())\\n\",\n \" for t, stats in deep_q_learning(sess,\\n\",\n \" env,\\n\",\n \" q_estimator=q_estimator,\\n\",\n \" target_estimator=target_estimator,\\n\",\n \" state_processor=state_processor,\\n\",\n \" experiment_dir=experiment_dir,\\n\",\n \" num_episodes=10000,\\n\",\n \" replay_memory_size=500000,\\n\",\n \" replay_memory_init_size=50000,\\n\",\n \" update_target_estimator_every=10000,\\n\",\n \" epsilon_start=1.0,\\n\",\n \" epsilon_end=0.1,\\n\",\n \" epsilon_decay_steps=500000,\\n\",\n \" discount_factor=0.99,\\n\",\n \" batch_size=32):\\n\",\n \"\\n\",\n \" print(\\\"\\\\nEpisode Reward: {}\\\".format(stats.episode_rewards[-1]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "DQN/Deep Q Learning.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"from gym.wrappers import Monitor\\n\",\n \"import itertools\\n\",\n \"import numpy as np\\n\",\n \"import os\\n\",\n \"import random\\n\",\n \"import sys\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\")\\n\",\n \"\\n\",\n \"from lib import plotting\\n\",\n \"from collections import deque, namedtuple\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"env = gym.envs.make(\\\"Breakout-v0\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Atari Actions: 0 (noop), 1 (fire), 2 (left) and 3 (right) are valid actions\\n\",\n \"VALID_ACTIONS = [0, 1, 2, 3]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class StateProcessor():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Processes a raw Atari images. Resizes it and converts it to grayscale.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def __init__(self):\\n\",\n \" # Build the Tensorflow graph\\n\",\n \" with tf.variable_scope(\\\"state_processor\\\"):\\n\",\n \" self.input_state = tf.placeholder(shape=[210, 160, 3], dtype=tf.uint8)\\n\",\n \" self.output = tf.image.rgb_to_grayscale(self.input_state)\\n\",\n \" self.output = tf.image.crop_to_bounding_box(self.output, 34, 0, 160, 160)\\n\",\n \" self.output = tf.image.resize_images(\\n\",\n \" self.output, [84, 84], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\\n\",\n \" self.output = tf.squeeze(self.output)\\n\",\n \"\\n\",\n \" def process(self, sess, state):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Args:\\n\",\n \" sess: A Tensorflow session object\\n\",\n \" state: A [210, 160, 3] Atari RGB State\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" A processed [84, 84] state representing grayscale values.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" return sess.run(self.output, { self.input_state: state })\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Estimator():\\n\",\n \" \\\"\\\"\\\"Q-Value Estimator neural network.\\n\",\n \"\\n\",\n \" This network is used for both the Q-Network and the Target Network.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" def __init__(self, scope=\\\"estimator\\\", summaries_dir=None):\\n\",\n \" self.scope = scope\\n\",\n \" # Writes Tensorboard summaries to disk\\n\",\n \" self.summary_writer = None\\n\",\n \" with tf.variable_scope(scope):\\n\",\n \" # Build the graph\\n\",\n \" self._build_model()\\n\",\n \" if summaries_dir:\\n\",\n \" summary_dir = os.path.join(summaries_dir, \\\"summaries_{}\\\".format(scope))\\n\",\n \" if not os.path.exists(summary_dir):\\n\",\n \" os.makedirs(summary_dir)\\n\",\n \" self.summary_writer = tf.summary.FileWriter(summary_dir)\\n\",\n \"\\n\",\n \" def _build_model(self):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Builds the Tensorflow graph.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Placeholders for our input\\n\",\n \" # Our input are 4 grayscale frames of shape 84, 84 each\\n\",\n \" self.X_pl = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.uint8, name=\\\"X\\\")\\n\",\n \" # The TD target value\\n\",\n \" self.y_pl = tf.placeholder(shape=[None], dtype=tf.float32, name=\\\"y\\\")\\n\",\n \" # Integer id of which action was selected\\n\",\n \" self.actions_pl = tf.placeholder(shape=[None], dtype=tf.int32, name=\\\"actions\\\")\\n\",\n \"\\n\",\n \" X = tf.to_float(self.X_pl) / 255.0\\n\",\n \" batch_size = tf.shape(self.X_pl)[0]\\n\",\n \"\\n\",\n \" # Three convolutional layers\\n\",\n \" conv1 = tf.contrib.layers.conv2d(\\n\",\n \" X, 32, 8, 4, activation_fn=tf.nn.relu)\\n\",\n \" conv2 = tf.contrib.layers.conv2d(\\n\",\n \" conv1, 64, 4, 2, activation_fn=tf.nn.relu)\\n\",\n \" conv3 = tf.contrib.layers.conv2d(\\n\",\n \" conv2, 64, 3, 1, activation_fn=tf.nn.relu)\\n\",\n \"\\n\",\n \" # Fully connected layers\\n\",\n \" flattened = tf.contrib.layers.flatten(conv3)\\n\",\n \" fc1 = tf.contrib.layers.fully_connected(flattened, 512)\\n\",\n \" self.predictions = tf.contrib.layers.fully_connected(fc1, len(VALID_ACTIONS))\\n\",\n \"\\n\",\n \" # Get the predictions for the chosen actions only\\n\",\n \" gather_indices = tf.range(batch_size) * tf.shape(self.predictions)[1] + self.actions_pl\\n\",\n \" self.action_predictions = tf.gather(tf.reshape(self.predictions, [-1]), gather_indices)\\n\",\n \"\\n\",\n \" # Calculate the loss\\n\",\n \" self.losses = tf.squared_difference(self.y_pl, self.action_predictions)\\n\",\n \" self.loss = tf.reduce_mean(self.losses)\\n\",\n \"\\n\",\n \" # Optimizer Parameters from original paper\\n\",\n \" self.optimizer = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6)\\n\",\n \" self.train_op = self.optimizer.minimize(self.loss, global_step=tf.contrib.framework.get_global_step())\\n\",\n \"\\n\",\n \" # Summaries for Tensorboard\\n\",\n \" self.summaries = tf.summary.merge([\\n\",\n \" tf.summary.scalar(\\\"loss\\\", self.loss),\\n\",\n \" tf.summary.histogram(\\\"loss_hist\\\", self.losses),\\n\",\n \" tf.summary.histogram(\\\"q_values_hist\\\", self.predictions),\\n\",\n \" tf.summary.scalar(\\\"max_q_value\\\", tf.reduce_max(self.predictions))\\n\",\n \" ])\\n\",\n \"\\n\",\n \"\\n\",\n \" def predict(self, sess, s):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Predicts action values.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" sess: Tensorflow session\\n\",\n \" s: State input of shape [batch_size, 4, 84, 84, 1]\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" Tensor of shape [batch_size, NUM_VALID_ACTIONS] containing the estimated \\n\",\n \" action values.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" return sess.run(self.predictions, { self.X_pl: s })\\n\",\n \"\\n\",\n \" def update(self, sess, s, a, y):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Updates the estimator towards the given targets.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" sess: Tensorflow session object\\n\",\n \" s: State input of shape [batch_size, 4, 84, 84, 1]\\n\",\n \" a: Chosen actions of shape [batch_size]\\n\",\n \" y: Targets of shape [batch_size]\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" The calculated loss on the batch.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" feed_dict = { self.X_pl: s, self.y_pl: y, self.actions_pl: a }\\n\",\n \" summaries, global_step, _, loss = sess.run(\\n\",\n \" [self.summaries, tf.contrib.framework.get_global_step(), self.train_op, self.loss],\\n\",\n \" feed_dict)\\n\",\n \" if self.summary_writer:\\n\",\n \" self.summary_writer.add_summary(summaries, global_step)\\n\",\n \" return loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# For Testing....\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"global_step = tf.Variable(0, name=\\\"global_step\\\", trainable=False)\\n\",\n \"\\n\",\n \"e = Estimator(scope=\\\"test\\\")\\n\",\n \"sp = StateProcessor()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(tf.global_variables_initializer())\\n\",\n \" \\n\",\n \" # Example observation batch\\n\",\n \" observation = env.reset()\\n\",\n \" \\n\",\n \" observation_p = sp.process(sess, observation)\\n\",\n \" observation = np.stack([observation_p] * 4, axis=2)\\n\",\n \" observations = np.array([observation] * 2)\\n\",\n \" \\n\",\n \" # Test Prediction\\n\",\n \" print(e.predict(sess, observations))\\n\",\n \"\\n\",\n \" # Test training step\\n\",\n \" y = np.array([10.0, 10.0])\\n\",\n \" a = np.array([1, 3])\\n\",\n \" print(e.update(sess, observations, a, y))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def copy_model_parameters(sess, estimator1, estimator2):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Copies the model parameters of one estimator to another.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" sess: Tensorflow session instance\\n\",\n \" estimator1: Estimator to copy the paramters from\\n\",\n \" estimator2: Estimator to copy the parameters to\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" e1_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator1.scope)]\\n\",\n \" e1_params = sorted(e1_params, key=lambda v: v.name)\\n\",\n \" e2_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator2.scope)]\\n\",\n \" e2_params = sorted(e2_params, key=lambda v: v.name)\\n\",\n \"\\n\",\n \" update_ops = []\\n\",\n \" for e1_v, e2_v in zip(e1_params, e2_params):\\n\",\n \" op = e2_v.assign(e1_v)\\n\",\n \" update_ops.append(op)\\n\",\n \"\\n\",\n \" sess.run(update_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def make_epsilon_greedy_policy(estimator, nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates an epsilon-greedy policy based on a given Q-function approximator and epsilon.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" estimator: An estimator that returns q values for a given state\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" A function that takes the (sess, observation, epsilon) as an argument and returns\\n\",\n \" the probabilities for each action in the form of a numpy array of length nA.\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def policy_fn(sess, observation, epsilon):\\n\",\n \" A = np.ones(nA, dtype=float) * epsilon / nA\\n\",\n \" q_values = estimator.predict(sess, np.expand_dims(observation, 0))[0]\\n\",\n \" best_action = np.argmax(q_values)\\n\",\n \" A[best_action] += (1.0 - epsilon)\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def deep_q_learning(sess,\\n\",\n \" env,\\n\",\n \" q_estimator,\\n\",\n \" target_estimator,\\n\",\n \" state_processor,\\n\",\n \" num_episodes,\\n\",\n \" experiment_dir,\\n\",\n \" replay_memory_size=500000,\\n\",\n \" replay_memory_init_size=50000,\\n\",\n \" update_target_estimator_every=10000,\\n\",\n \" discount_factor=0.99,\\n\",\n \" epsilon_start=1.0,\\n\",\n \" epsilon_end=0.1,\\n\",\n \" epsilon_decay_steps=500000,\\n\",\n \" batch_size=32,\\n\",\n \" record_video_every=50):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Q-Learning algorithm for off-policy TD control using Function Approximation.\\n\",\n \" Finds the optimal greedy policy while following an epsilon-greedy policy.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" sess: Tensorflow Session object\\n\",\n \" env: OpenAI environment\\n\",\n \" q_estimator: Estimator object used for the q values\\n\",\n \" target_estimator: Estimator object used for the targets\\n\",\n \" state_processor: A StateProcessor object\\n\",\n \" num_episodes: Number of episodes to run for\\n\",\n \" experiment_dir: Directory to save Tensorflow summaries in\\n\",\n \" replay_memory_size: Size of the replay memory\\n\",\n \" replay_memory_init_size: Number of random experiences to sampel when initializing \\n\",\n \" the reply memory.\\n\",\n \" update_target_estimator_every: Copy parameters from the Q estimator to the \\n\",\n \" target estimator every N steps\\n\",\n \" discount_factor: Gamma discount factor\\n\",\n \" epsilon_start: Chance to sample a random action when taking an action.\\n\",\n \" Epsilon is decayed over time and this is the start value\\n\",\n \" epsilon_end: The final minimum value of epsilon after decaying is done\\n\",\n \" epsilon_decay_steps: Number of steps to decay epsilon over\\n\",\n \" batch_size: Size of batches to sample from the replay memory\\n\",\n \" record_video_every: Record a video every N episodes\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" Transition = namedtuple(\\\"Transition\\\", [\\\"state\\\", \\\"action\\\", \\\"reward\\\", \\\"next_state\\\", \\\"done\\\"])\\n\",\n \"\\n\",\n \" # The replay memory\\n\",\n \" replay_memory = []\\n\",\n \"\\n\",\n \" # Keeps track of useful statistics\\n\",\n \" stats = plotting.EpisodeStats(\\n\",\n \" episode_lengths=np.zeros(num_episodes),\\n\",\n \" episode_rewards=np.zeros(num_episodes))\\n\",\n \"\\n\",\n \" # Create directories for checkpoints and summaries\\n\",\n \" checkpoint_dir = os.path.join(experiment_dir, \\\"checkpoints\\\")\\n\",\n \" checkpoint_path = os.path.join(checkpoint_dir, \\\"model\\\")\\n\",\n \" monitor_path = os.path.join(experiment_dir, \\\"monitor\\\")\\n\",\n \"\\n\",\n \" if not os.path.exists(checkpoint_dir):\\n\",\n \" os.makedirs(checkpoint_dir)\\n\",\n \" if not os.path.exists(monitor_path):\\n\",\n \" os.makedirs(monitor_path)\\n\",\n \"\\n\",\n \" saver = tf.train.Saver()\\n\",\n \" # Load a previous checkpoint if we find one\\n\",\n \" latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)\\n\",\n \" if latest_checkpoint:\\n\",\n \" print(\\\"Loading model checkpoint {}...\\\\n\\\".format(latest_checkpoint))\\n\",\n \" saver.restore(sess, latest_checkpoint)\\n\",\n \" \\n\",\n \" # Get the current time step\\n\",\n \" total_t = sess.run(tf.contrib.framework.get_global_step())\\n\",\n \"\\n\",\n \" # The epsilon decay schedule\\n\",\n \" epsilons = np.linspace(epsilon_start, epsilon_end, epsilon_decay_steps)\\n\",\n \"\\n\",\n \" # The policy we're following\\n\",\n \" policy = make_epsilon_greedy_policy(\\n\",\n \" q_estimator,\\n\",\n \" len(VALID_ACTIONS))\\n\",\n \"\\n\",\n \" # Populate the replay memory with initial experience\\n\",\n \" print(\\\"Populating replay memory...\\\")\\n\",\n \" state = env.reset()\\n\",\n \" state = state_processor.process(sess, state)\\n\",\n \" state = np.stack([state] * 4, axis=2)\\n\",\n \" for i in range(replay_memory_init_size):\\n\",\n \" # TODO: Populate replay memory!\\n\",\n \" pass\\n\",\n \"\\n\",\n \" # Record videos\\n\",\n \" env= Monitor(env,\\n\",\n \" directory=monitor_path,\\n\",\n \" resume=True,\\n\",\n \" video_callable=lambda count: count % record_video_every == 0)\\n\",\n \"\\n\",\n \" for i_episode in range(num_episodes):\\n\",\n \"\\n\",\n \" # Save the current checkpoint\\n\",\n \" saver.save(tf.get_default_session(), checkpoint_path)\\n\",\n \"\\n\",\n \" # Reset the environment\\n\",\n \" state = env.reset()\\n\",\n \" state = state_processor.process(sess, state)\\n\",\n \" state = np.stack([state] * 4, axis=2)\\n\",\n \" loss = None\\n\",\n \"\\n\",\n \" # One step in the environment\\n\",\n \" for t in itertools.count():\\n\",\n \"\\n\",\n \" # Epsilon for this time step\\n\",\n \" epsilon = epsilons[min(total_t, epsilon_decay_steps-1)]\\n\",\n \"\\n\",\n \" # Add epsilon to Tensorboard\\n\",\n \" episode_summary = tf.Summary()\\n\",\n \" episode_summary.value.add(simple_value=epsilon, tag=\\\"epsilon\\\")\\n\",\n \" q_estimator.summary_writer.add_summary(episode_summary, total_t)\\n\",\n \"\\n\",\n \" # TODO: Maybe update the target estimator\\n\",\n \" if total_t % update_target_estimator_every == 0:\\n\",\n \" pass\\n\",\n \"\\n\",\n \" # Print out which step we're on, useful for debugging.\\n\",\n \" print(\\\"\\\\rStep {} ({}) @ Episode {}/{}, loss: {}\\\".format(\\n\",\n \" t, total_t, i_episode + 1, num_episodes, loss), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \"\\n\",\n \" # Take a step in the environment\\n\",\n \" # TODO: Implement!\\n\",\n \"\\n\",\n \" # If our replay memory is full, pop the first element\\n\",\n \" if len(replay_memory) == replay_memory_size:\\n\",\n \" replay_memory.pop(0)\\n\",\n \"\\n\",\n \" # TODO: Save transition to replay memory\\n\",\n \"\\n\",\n \" # Update statistics\\n\",\n \" stats.episode_rewards[i_episode] += reward\\n\",\n \" stats.episode_lengths[i_episode] = t\\n\",\n \"\\n\",\n \" # TODO: Sample a minibatch from the replay memory\\n\",\n \" # TODO: Calculate q values and targets\\n\",\n \" # TODO Perform gradient descent update\\n\",\n \"\\n\",\n \" if done:\\n\",\n \" break\\n\",\n \"\\n\",\n \" state = next_state\\n\",\n \" total_t += 1\\n\",\n \"\\n\",\n \" # Add summaries to tensorboard\\n\",\n \" episode_summary = tf.Summary()\\n\",\n \" episode_summary.value.add(simple_value=stats.episode_rewards[i_episode], node_name=\\\"episode_reward\\\", tag=\\\"episode_reward\\\")\\n\",\n \" episode_summary.value.add(simple_value=stats.episode_lengths[i_episode], node_name=\\\"episode_length\\\", tag=\\\"episode_length\\\")\\n\",\n \" q_estimator.summary_writer.add_summary(episode_summary, total_t)\\n\",\n \" q_estimator.summary_writer.flush()\\n\",\n \"\\n\",\n \" yield total_t, plotting.EpisodeStats(\\n\",\n \" episode_lengths=stats.episode_lengths[:i_episode+1],\\n\",\n \" episode_rewards=stats.episode_rewards[:i_episode+1])\\n\",\n \"\\n\",\n \" env.monitor.close()\\n\",\n \" return stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"# Where we save our checkpoints and graphs\\n\",\n \"experiment_dir = os.path.abspath(\\\"./experiments/{}\\\".format(env.spec.id))\\n\",\n \"\\n\",\n \"# Create a glboal step variable\\n\",\n \"global_step = tf.Variable(0, name='global_step', trainable=False)\\n\",\n \" \\n\",\n \"# Create estimators\\n\",\n \"q_estimator = Estimator(scope=\\\"q\\\", summaries_dir=experiment_dir)\\n\",\n \"target_estimator = Estimator(scope=\\\"target_q\\\")\\n\",\n \"\\n\",\n \"# State processor\\n\",\n \"state_processor = StateProcessor()\\n\",\n \"\\n\",\n \"# Run it!\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(tf.initialize_all_variables())\\n\",\n \" for t, stats in deep_q_learning(sess,\\n\",\n \" env,\\n\",\n \" q_estimator=q_estimator,\\n\",\n \" target_estimator=target_estimator,\\n\",\n \" state_processor=state_processor,\\n\",\n \" experiment_dir=experiment_dir,\\n\",\n \" num_episodes=10000,\\n\",\n \" replay_memory_size=500000,\\n\",\n \" replay_memory_init_size=50000,\\n\",\n \" update_target_estimator_every=10000,\\n\",\n \" epsilon_start=1.0,\\n\",\n \" epsilon_end=0.1,\\n\",\n \" epsilon_decay_steps=500000,\\n\",\n \" discount_factor=0.99,\\n\",\n \" batch_size=32):\\n\",\n \"\\n\",\n \" print(\\\"\\\\nEpisode Reward: {}\\\".format(stats.episode_rewards[-1]))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "DQN/Double DQN Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import itertools\\n\",\n \"import numpy as np\\n\",\n \"import os\\n\",\n \"import random\\n\",\n \"import sys\\n\",\n \"import tensorflow as tf\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\")\\n\",\n \"\\n\",\n \"from lib import plotting\\n\",\n \"from collections import deque, namedtuple\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"env = gym.envs.make(\\\"Breakout-v0\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Atari Actions: 0 (noop), 1 (fire), 2 (left) and 3 (right) are valid actions\\n\",\n \"VALID_ACTIONS = [0, 1, 2, 3]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class StateProcessor():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Processes a raw Atari images. Resizes it and converts it to grayscale.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def __init__(self):\\n\",\n \" # Build the Tensorflow graph\\n\",\n \" with tf.variable_scope(\\\"state_processor\\\"):\\n\",\n \" self.input_state = tf.placeholder(shape=[210, 160, 3], dtype=tf.uint8)\\n\",\n \" self.output = tf.image.rgb_to_grayscale(self.input_state)\\n\",\n \" self.output = tf.image.crop_to_bounding_box(self.output, 34, 0, 160, 160)\\n\",\n \" self.output = tf.image.resize_images(\\n\",\n \" self.output, 84, 84, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\\n\",\n \" self.output = tf.squeeze(self.output)\\n\",\n \"\\n\",\n \" def process(self, sess, state):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Args:\\n\",\n \" sess: A Tensorflow session object\\n\",\n \" state: A [210, 160, 3] Atari RGB State\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" A processed [84, 84] state representing grayscale values.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" return sess.run(self.output, { self.input_state: state })\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Estimator():\\n\",\n \" \\\"\\\"\\\"Q-Value Estimator neural network.\\n\",\n \"\\n\",\n \" This network is used for both the Q-Network and the Target Network.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" def __init__(self, scope=\\\"estimator\\\", summaries_dir=None):\\n\",\n \" self.scope = scope\\n\",\n \" # Writes Tensorboard summaries to disk\\n\",\n \" self.summary_writer = None\\n\",\n \" with tf.variable_scope(scope):\\n\",\n \" # Build the graph\\n\",\n \" self._build_model()\\n\",\n \" if summaries_dir:\\n\",\n \" summary_dir = os.path.join(summaries_dir, \\\"summaries_{}\\\".format(scope))\\n\",\n \" if not os.path.exists(summary_dir):\\n\",\n \" os.makedirs(summary_dir)\\n\",\n \" self.summary_writer = tf.train.SummaryWriter(summary_dir)\\n\",\n \"\\n\",\n \" def _build_model(self):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Builds the Tensorflow graph.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Placeholders for our input\\n\",\n \" # Our input are 4 grayscale frames of shape 84, 84 each\\n\",\n \" self.X_pl = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.uint8, name=\\\"X\\\")\\n\",\n \" # The TD target value\\n\",\n \" self.y_pl = tf.placeholder(shape=[None], dtype=tf.float32, name=\\\"y\\\")\\n\",\n \" # Integer id of which action was selected\\n\",\n \" self.actions_pl = tf.placeholder(shape=[None], dtype=tf.int32, name=\\\"actions\\\")\\n\",\n \"\\n\",\n \" X = tf.to_float(self.X_pl) / 255.0\\n\",\n \" \\n\",\n \" # TODO: Implement the Tensorflow graph!\\n\",\n \" batch_size = tf.shape(self.X_pl)[0]\\n\",\n \" self.predictions = tf.zeros(shape=[batch_size, len(VALID_ACTIONS)])\\n\",\n \" self.loss = tf.constant(0.0)\\n\",\n \" self.train_op = tf.no_op(\\\"train_pp\\\")\\n\",\n \" \\n\",\n \" # Summaries for Tensorboard\\n\",\n \" self.summaries = tf.merge_summary([\\n\",\n \" tf.scalar_summary(\\\"loss\\\", self.loss)\\n\",\n \" ])\\n\",\n \"\\n\",\n \"\\n\",\n \" def predict(self, sess, s):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Predicts action values.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" sess: Tensorflow session\\n\",\n \" s: State input of shape [batch_size, 4, 84, 84, 1]\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" Tensor of shape [batch_size, NUM_VALID_ACTIONS] containing the estimated \\n\",\n \" action values.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" return sess.run(self.predictions, { self.X_pl: s })\\n\",\n \"\\n\",\n \" def update(self, sess, s, a, y):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Updates the estimator towards the given targets.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" sess: Tensorflow session object\\n\",\n \" s: State input of shape [batch_size, 4, 84, 84, 1]\\n\",\n \" a: Chosen actions of shape [batch_size]\\n\",\n \" y: Targets of shape [batch_size]\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" The calculated loss on the batch.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" feed_dict = { self.X_pl: s, self.y_pl: y, self.actions_pl: a }\\n\",\n \" summaries, global_step, _, loss = sess.run(\\n\",\n \" [self.summaries, tf.contrib.framework.get_global_step(), self.train_op, self.loss],\\n\",\n \" feed_dict)\\n\",\n \" if self.summary_writer:\\n\",\n \" self.summary_writer.add_summary(summaries, global_step)\\n\",\n \" return loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# For Testing....\\n\",\n \"\\n\",\n \"tf.reset_default_graph()\\n\",\n \"global_step = tf.Variable(0, name=\\\"global_step\\\", trainable=False)\\n\",\n \"\\n\",\n \"e = Estimator(scope=\\\"test\\\")\\n\",\n \"sp = StateProcessor()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(tf.initialize_all_variables())\\n\",\n \" \\n\",\n \" # Example observation batch\\n\",\n \" observation = env.reset()\\n\",\n \" \\n\",\n \" observation_p = sp.process(sess, observation)\\n\",\n \" observation = np.stack([observation_p] * 4, axis=2)\\n\",\n \" observations = np.array([observation] * 2)\\n\",\n \" \\n\",\n \" # Test Prediction\\n\",\n \" print(e.predict(sess, observations))\\n\",\n \"\\n\",\n \" # Test training step\\n\",\n \" y = np.array([10.0, 10.0])\\n\",\n \" a = np.array([1, 3])\\n\",\n \" print(e.update(sess, observations, a, y))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def copy_model_parameters(sess, estimator1, estimator2):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Copies the model parameters of one estimator to another.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" sess: Tensorflow session instance\\n\",\n \" estimator1: Estimator to copy the paramters from\\n\",\n \" estimator2: Estimator to copy the parameters to\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" e1_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator1.scope)]\\n\",\n \" e1_params = sorted(e1_params, key=lambda v: v.name)\\n\",\n \" e2_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator2.scope)]\\n\",\n \" e2_params = sorted(e2_params, key=lambda v: v.name)\\n\",\n \"\\n\",\n \" update_ops = []\\n\",\n \" for e1_v, e2_v in zip(e1_params, e2_params):\\n\",\n \" op = e2_v.assign(e1_v)\\n\",\n \" update_ops.append(op)\\n\",\n \"\\n\",\n \" sess.run(update_ops)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def make_epsilon_greedy_policy(estimator, nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates an epsilon-greedy policy based on a given Q-function approximator and epsilon.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" estimator: An estimator that returns q values for a given state\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" A function that takes the (sess, observation, epsilon) as an argument and returns\\n\",\n \" the probabilities for each action in the form of a numpy array of length nA.\\n\",\n \"\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def policy_fn(sess, observation, epsilon):\\n\",\n \" A = np.ones(nA, dtype=float) * epsilon / nA\\n\",\n \" q_values = estimator.predict(sess, np.expand_dims(observation, 0))[0]\\n\",\n \" best_action = np.argmax(q_values)\\n\",\n \" A[best_action] += (1.0 - epsilon)\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def deep_q_learning(sess,\\n\",\n \" env,\\n\",\n \" q_estimator,\\n\",\n \" target_estimator,\\n\",\n \" state_processor,\\n\",\n \" num_episodes,\\n\",\n \" experiment_dir,\\n\",\n \" replay_memory_size=500000,\\n\",\n \" replay_memory_init_size=50000,\\n\",\n \" update_target_estimator_every=10000,\\n\",\n \" discount_factor=0.99,\\n\",\n \" epsilon_start=1.0,\\n\",\n \" epsilon_end=0.1,\\n\",\n \" epsilon_decay_steps=500000,\\n\",\n \" batch_size=32,\\n\",\n \" record_video_every=50):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Q-Learning algorithm for off-policy TD control using Function Approximation.\\n\",\n \" Finds the optimal greedy policy while following an epsilon-greedy policy.\\n\",\n \"\\n\",\n \" Args:\\n\",\n \" sess: Tensorflow Session object\\n\",\n \" env: OpenAI environment\\n\",\n \" q_estimator: Estimator object used for the q values\\n\",\n \" target_estimator: Estimator object used for the targets\\n\",\n \" state_processor: A StateProcessor object\\n\",\n \" num_episodes: Number of episodes to run for\\n\",\n \" experiment_dir: Directory to save Tensorflow summaries in\\n\",\n \" replay_memory_size: Size of the replay memory\\n\",\n \" replay_memory_init_size: Number of random experiences to sampel when initializing \\n\",\n \" the reply memory.\\n\",\n \" update_target_estimator_every: Copy parameters from the Q estimator to the \\n\",\n \" target estimator every N steps\\n\",\n \" discount_factor: Gamma discount factor\\n\",\n \" epsilon_start: Chance to sample a random action when taking an action.\\n\",\n \" Epsilon is decayed over time and this is the start value\\n\",\n \" epsilon_end: The final minimum value of epsilon after decaying is done\\n\",\n \" epsilon_decay_steps: Number of steps to decay epsilon over\\n\",\n \" batch_size: Size of batches to sample from the replay memory\\n\",\n \" record_video_every: Record a video every N episodes\\n\",\n \"\\n\",\n \" Returns:\\n\",\n \" An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" Transition = namedtuple(\\\"Transition\\\", [\\\"state\\\", \\\"action\\\", \\\"reward\\\", \\\"next_state\\\", \\\"done\\\"])\\n\",\n \"\\n\",\n \" # The replay memory\\n\",\n \" replay_memory = []\\n\",\n \"\\n\",\n \" # Keeps track of useful statistics\\n\",\n \" stats = plotting.EpisodeStats(\\n\",\n \" episode_lengths=np.zeros(num_episodes),\\n\",\n \" episode_rewards=np.zeros(num_episodes))\\n\",\n \"\\n\",\n \" # Create directories for checkpoints and summaries\\n\",\n \" checkpoint_dir = os.path.join(experiment_dir, \\\"checkpoints\\\")\\n\",\n \" checkpoint_path = os.path.join(checkpoint_dir, \\\"model\\\")\\n\",\n \" monitor_path = os.path.join(experiment_dir, \\\"monitor\\\")\\n\",\n \"\\n\",\n \" if not os.path.exists(checkpoint_dir):\\n\",\n \" os.makedirs(checkpoint_dir)\\n\",\n \" if not os.path.exists(monitor_path):\\n\",\n \" os.makedirs(monitor_path)\\n\",\n \"\\n\",\n \" saver = tf.train.Saver()\\n\",\n \" # Load a previous checkpoint if we find one\\n\",\n \" latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)\\n\",\n \" if latest_checkpoint:\\n\",\n \" print(\\\"Loading model checkpoint {}...\\\\n\\\".format(latest_checkpoint))\\n\",\n \" saver.restore(sess, latest_checkpoint)\\n\",\n \" \\n\",\n \" # Get the current time step\\n\",\n \" total_t = sess.run(tf.contrib.framework.get_global_step())\\n\",\n \"\\n\",\n \" # The epsilon decay schedule\\n\",\n \" epsilons = np.linspace(epsilon_start, epsilon_end, epsilon_decay_steps)\\n\",\n \"\\n\",\n \" # The policy we're following\\n\",\n \" policy = make_epsilon_greedy_policy(\\n\",\n \" q_estimator,\\n\",\n \" len(VALID_ACTIONS))\\n\",\n \"\\n\",\n \" # Populate the replay memory with initial experience\\n\",\n \" print(\\\"Populating replay memory...\\\")\\n\",\n \" state = env.reset()\\n\",\n \" state = state_processor.process(sess, state)\\n\",\n \" state = np.stack([state] * 4, axis=2)\\n\",\n \" for i in range(replay_memory_init_size):\\n\",\n \" action_probs = policy(sess, state, epsilons[total_t])\\n\",\n \" action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\\n\",\n \" next_state, reward, done, _ = env.step(VALID_ACTIONS[action])\\n\",\n \" next_state = state_processor.process(sess, next_state)\\n\",\n \" next_state = np.append(state[:,:,1:], np.expand_dims(next_state, 2), axis=2)\\n\",\n \" replay_memory.append(Transition(state, action, reward, next_state, done))\\n\",\n \" if done:\\n\",\n \" state = env.reset()\\n\",\n \" state = state_processor.process(sess, state)\\n\",\n \" state = np.stack([state] * 4, axis=2)\\n\",\n \" else:\\n\",\n \" state = next_state\\n\",\n \"\\n\",\n \" # Record videos\\n\",\n \" env.monitor.start(monitor_path,\\n\",\n \" resume=True,\\n\",\n \" video_callable=lambda count: count % record_video_every == 0)\\n\",\n \"\\n\",\n \" for i_episode in range(num_episodes):\\n\",\n \"\\n\",\n \" # Save the current checkpoint\\n\",\n \" saver.save(tf.get_default_session(), checkpoint_path)\\n\",\n \"\\n\",\n \" # Reset the environment\\n\",\n \" state = env.reset()\\n\",\n \" state = state_processor.process(sess, state)\\n\",\n \" state = np.stack([state] * 4, axis=2)\\n\",\n \" loss = None\\n\",\n \"\\n\",\n \" # One step in the environment\\n\",\n \" for t in itertools.count():\\n\",\n \"\\n\",\n \" # Epsilon for this time step\\n\",\n \" epsilon = epsilons[min(total_t, epsilon_decay_steps-1)]\\n\",\n \"\\n\",\n \" # Add epsilon to Tensorboard\\n\",\n \" episode_summary = tf.Summary()\\n\",\n \" episode_summary.value.add(simple_value=epsilon, tag=\\\"epsilon\\\")\\n\",\n \" q_estimator.summary_writer.add_summary(episode_summary, total_t)\\n\",\n \"\\n\",\n \" # Maybe update the target estimator\\n\",\n \" if total_t % update_target_estimator_every == 0:\\n\",\n \" copy_model_parameters(sess, q_estimator, target_estimator)\\n\",\n \" print(\\\"\\\\nCopied model parameters to target network.\\\")\\n\",\n \"\\n\",\n \" # Print out which step we're on, useful for debugging.\\n\",\n \" print(\\\"\\\\rStep {} ({}) @ Episode {}/{}, loss: {}\\\".format(\\n\",\n \" t, total_t, i_episode + 1, num_episodes, loss), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \"\\n\",\n \" # Take a step\\n\",\n \" action_probs = policy(sess, state, epsilon)\\n\",\n \" action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\\n\",\n \" next_state, reward, done, _ = env.step(VALID_ACTIONS[action])\\n\",\n \" next_state = state_processor.process(sess, next_state)\\n\",\n \" next_state = np.append(state[:,:,1:], np.expand_dims(next_state, 2), axis=2)\\n\",\n \"\\n\",\n \" # If our replay memory is full, pop the first element\\n\",\n \" if len(replay_memory) == replay_memory_size:\\n\",\n \" replay_memory.pop(0)\\n\",\n \"\\n\",\n \" # Save transition to replay memory\\n\",\n \" replay_memory.append(Transition(state, action, reward, next_state, done)) \\n\",\n \"\\n\",\n \" # Update statistics\\n\",\n \" stats.episode_rewards[i_episode] += reward\\n\",\n \" stats.episode_lengths[i_episode] = t\\n\",\n \"\\n\",\n \" # Sample a minibatch from the replay memory\\n\",\n \" samples = random.sample(replay_memory, batch_size)\\n\",\n \" states_batch, action_batch, reward_batch, next_states_batch, done_batch = map(np.array, zip(*samples))\\n\",\n \"\\n\",\n \" # Calculate q values and targets\\n\",\n \" # This is where Double Q-Learning comes in!\\n\",\n \" q_values_next = q_estimator.predict(sess, next_states_batch)\\n\",\n \" best_actions = np.argmax(q_values_next, axis=1)\\n\",\n \" q_values_next_target = target_estimator.predict(sess, next_states_batch)\\n\",\n \" targets_batch = reward_batch + np.invert(done_batch).astype(np.float32) * \\\\\\n\",\n \" discount_factor * q_values_next_target[np.arange(batch_size), best_actions]\\n\",\n \"\\n\",\n \" # Perform gradient descent update\\n\",\n \" states_batch = np.array(states_batch)\\n\",\n \" loss = q_estimator.update(sess, states_batch, action_batch, targets_batch)\\n\",\n \"\\n\",\n \" if done:\\n\",\n \" break\\n\",\n \"\\n\",\n \" state = next_state\\n\",\n \" total_t += 1\\n\",\n \"\\n\",\n \" # Add summaries to tensorboard\\n\",\n \" episode_summary = tf.Summary()\\n\",\n \" episode_summary.value.add(simple_value=stats.episode_rewards[i_episode], node_name=\\\"episode_reward\\\", tag=\\\"episode_reward\\\")\\n\",\n \" episode_summary.value.add(simple_value=stats.episode_lengths[i_episode], node_name=\\\"episode_length\\\", tag=\\\"episode_length\\\")\\n\",\n \" q_estimator.summary_writer.add_summary(episode_summary, total_t)\\n\",\n \" q_estimator.summary_writer.flush()\\n\",\n \"\\n\",\n \" yield total_t, plotting.EpisodeStats(\\n\",\n \" episode_lengths=stats.episode_lengths[:i_episode+1],\\n\",\n \" episode_rewards=stats.episode_rewards[:i_episode+1])\\n\",\n \"\\n\",\n \" env.monitor.close()\\n\",\n \" return stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"# Where we save our checkpoints and graphs\\n\",\n \"experiment_dir = os.path.abspath(\\\"./experiments/{}\\\".format(env.spec.id))\\n\",\n \"\\n\",\n \"# Create a glboal step variable\\n\",\n \"global_step = tf.Variable(0, name='global_step', trainable=False)\\n\",\n \" \\n\",\n \"# Create estimators\\n\",\n \"q_estimator = Estimator(scope=\\\"q\\\", summaries_dir=experiment_dir)\\n\",\n \"target_estimator = Estimator(scope=\\\"target_q\\\")\\n\",\n \"\\n\",\n \"# State processor\\n\",\n \"state_processor = StateProcessor()\\n\",\n \"\\n\",\n \"# Run it!\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(tf.initialize_all_variables())\\n\",\n \" for t, stats in deep_q_learning(sess,\\n\",\n \" env,\\n\",\n \" q_estimator=q_estimator,\\n\",\n \" target_estimator=target_estimator,\\n\",\n \" state_processor=state_processor,\\n\",\n \" experiment_dir=experiment_dir,\\n\",\n \" num_episodes=10000,\\n\",\n \" replay_memory_size=500000,\\n\",\n \" replay_memory_init_size=50000,\\n\",\n \" update_target_estimator_every=10000,\\n\",\n \" epsilon_start=1.0,\\n\",\n \" epsilon_end=0.1,\\n\",\n \" epsilon_decay_steps=500000,\\n\",\n \" discount_factor=0.99,\\n\",\n \" batch_size=32):\\n\",\n \"\\n\",\n \" print(\\\"\\\\nEpisode Reward: {}\\\".format(stats.episode_rewards[-1]))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "DQN/README.md", "content": "## Deep Q-Learning\n\n### Learning Goals\n\n- Understand the Deep Q-Learning (DQN) algorithm\n- Understand why Experience Replay and a Target Network are necessary to make Deep Q-Learning work in practice\n- (Optional) Understand Double Deep Q-Learning\n- (Optional) Understand Prioritized Experience Replay\n\n\n### Summary\n\n- DQN: Q-Learning but with a Deep Neural Network as a function approximator.\n- Using a non-linear Deep Neural Network is powerful, but training is unstable if we apply it naively.\n- Trick 1 - Experience Replay: Store experience `(S, A, R, S_next)` in a replay buffer and sample minibatches from it to train the network. This decorrelates the data and leads to better data efficiency. In the beginning, the replay buffer is filled with random experience.\n- Trick 2 - Target Network: Use a separate network to estimate the TD target. This target network has the same architecture as the function approximator but with frozen parameters. Every T steps (a hyperparameter) the parameters from the Q network are copied to the target network. This leads to more stable training because it keeps the target function fixed (for a while).\n- By using a Convolutional Neural Network as the function approximator on raw pixels of Atari games where the score is the reward we can learn to play many of those games at human-like performance.\n- Double DQN: Just like regular Q-Learning, DQN tends to overestimate values due to its max operation applied to both selecting and estimating actions. We get around this by using the Q network for selection and the target network for estimation when making updates.\n\n\n### Lectures & Readings\n\n**Required:**\n\n- [Human-Level Control through Deep Reinforcement Learning](http://www.readcube.com/articles/10.1038/nature14236)\n- [Demystifying Deep Reinforcement Learning](https://ai.intel.com/demystifying-deep-reinforcement-learning/)\n- David Silver's RL Course Lecture 6 - Value Function Approximation ([video](https://www.youtube.com/watch?v=UoPei5o4fps), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/FA.pdf))\n\n**Optional:**\n\n- [Using Keras and Deep Q-Network to Play FlappyBird](https://yanpanlau.github.io/2016/07/10/FlappyBird-Keras.html)\n- [Deep Reinforcement Learning with Double Q-learning](http://arxiv.org/abs/1509.06461)\n- [Prioritized Experience Replay](http://arxiv.org/abs/1511.05952)\n\n**Deep Learning:**\n\n- [Tensorflow](http://www.tensorflow.org)\n- [Deep Learning Books](http://www.deeplearningbook.org/)\n\n### Exercises\n\n- Get familiar with the [OpenAI Gym Atari Environment Playground](Breakout%20Playground.ipynb)\n- Deep-Q Learning for Atari Games\n - [Exercise](Deep%20Q%20Learning.ipynb)\n - [Solution](Deep%20Q%20Learning%20Solution.ipynb)\n- Double-Q Learning\n - This is a minimal change to Q-Learning so use the same exercise as above\n - [Solution](Double%20DQN%20Solution.ipynb)\n- Prioritized Experience Replay (WIP)\n" }, { "path": "DQN/dqn.py", "content": "import gym\nfrom gym.wrappers import Monitor\nimport itertools\nimport numpy as np\nimport os\nimport random\nimport sys\nimport tensorflow as tf\n\nif \"../\" not in sys.path:\n sys.path.append(\"../\")\n\nfrom lib import plotting\nfrom collections import deque, namedtuple\n\nenv = gym.envs.make(\"Breakout-v0\")\n\n# Atari Actions: 0 (noop), 1 (fire), 2 (left) and 3 (right) are valid actions\nVALID_ACTIONS = [0, 1, 2, 3]\n\nclass StateProcessor():\n \"\"\"\n Processes a raw Atari images. Resizes it and converts it to grayscale.\n \"\"\"\n def __init__(self):\n # Build the Tensorflow graph\n with tf.variable_scope(\"state_processor\"):\n self.input_state = tf.placeholder(shape=[210, 160, 3], dtype=tf.uint8)\n self.output = tf.image.rgb_to_grayscale(self.input_state)\n self.output = tf.image.crop_to_bounding_box(self.output, 34, 0, 160, 160)\n self.output = tf.image.resize_images(\n self.output, [84, 84], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n self.output = tf.squeeze(self.output)\n\n def process(self, sess, state):\n \"\"\"\n Args:\n sess: A Tensorflow session object\n state: A [210, 160, 3] Atari RGB State\n\n Returns:\n A processed [84, 84] state representing grayscale values.\n \"\"\"\n return sess.run(self.output, { self.input_state: state })\n\nclass Estimator():\n \"\"\"Q-Value Estimator neural network.\n\n This network is used for both the Q-Network and the Target Network.\n \"\"\"\n\n def __init__(self, scope=\"estimator\", summaries_dir=None):\n self.scope = scope\n # Writes Tensorboard summaries to disk\n self.summary_writer = None\n with tf.variable_scope(scope):\n # Build the graph\n self._build_model()\n if summaries_dir:\n summary_dir = os.path.join(summaries_dir, \"summaries_{}\".format(scope))\n if not os.path.exists(summary_dir):\n os.makedirs(summary_dir)\n self.summary_writer = tf.summary.FileWriter(summary_dir)\n\n def _build_model(self):\n \"\"\"\n Builds the Tensorflow graph.\n \"\"\"\n\n # Placeholders for our input\n # Our input are 4 RGB frames of shape 160, 160 each\n self.X_pl = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.uint8, name=\"X\")\n # The TD target value\n self.y_pl = tf.placeholder(shape=[None], dtype=tf.float32, name=\"y\")\n # Integer id of which action was selected\n self.actions_pl = tf.placeholder(shape=[None], dtype=tf.int32, name=\"actions\")\n\n X = tf.to_float(self.X_pl) / 255.0\n batch_size = tf.shape(self.X_pl)[0]\n\n # Three convolutional layers\n conv1 = tf.contrib.layers.conv2d(\n X, 32, 8, 4, activation_fn=tf.nn.relu)\n conv2 = tf.contrib.layers.conv2d(\n conv1, 64, 4, 2, activation_fn=tf.nn.relu)\n conv3 = tf.contrib.layers.conv2d(\n conv2, 64, 3, 1, activation_fn=tf.nn.relu)\n\n # Fully connected layers\n flattened = tf.contrib.layers.flatten(conv3)\n fc1 = tf.contrib.layers.fully_connected(flattened, 512)\n self.predictions = tf.contrib.layers.fully_connected(fc1, len(VALID_ACTIONS))\n\n # Get the predictions for the chosen actions only\n gather_indices = tf.range(batch_size) * tf.shape(self.predictions)[1] + self.actions_pl\n self.action_predictions = tf.gather(tf.reshape(self.predictions, [-1]), gather_indices)\n\n # Calculate the loss\n self.losses = tf.squared_difference(self.y_pl, self.action_predictions)\n self.loss = tf.reduce_mean(self.losses)\n\n # Optimizer Parameters from original paper\n self.optimizer = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6)\n self.train_op = self.optimizer.minimize(self.loss, global_step=tf.contrib.framework.get_global_step())\n\n # Summaries for Tensorboard\n self.summaries = tf.summary.merge([\n tf.summary.scalar(\"loss\", self.loss),\n tf.summary.histogram(\"loss_hist\", self.losses),\n tf.summary.histogram(\"q_values_hist\", self.predictions),\n tf.summary.scalar(\"max_q_value\", tf.reduce_max(self.predictions))\n ])\n\n\n def predict(self, sess, s):\n \"\"\"\n Predicts action values.\n\n Args:\n sess: Tensorflow session\n s: State input of shape [batch_size, 4, 160, 160, 3]\n\n Returns:\n Tensor of shape [batch_size, NUM_VALID_ACTIONS] containing the estimated \n action values.\n \"\"\"\n return sess.run(self.predictions, { self.X_pl: s })\n\n def update(self, sess, s, a, y):\n \"\"\"\n Updates the estimator towards the given targets.\n\n Args:\n sess: Tensorflow session object\n s: State input of shape [batch_size, 4, 160, 160, 3]\n a: Chosen actions of shape [batch_size]\n y: Targets of shape [batch_size]\n\n Returns:\n The calculated loss on the batch.\n \"\"\"\n feed_dict = { self.X_pl: s, self.y_pl: y, self.actions_pl: a }\n summaries, global_step, _, loss = sess.run(\n [self.summaries, tf.contrib.framework.get_global_step(), self.train_op, self.loss],\n feed_dict)\n if self.summary_writer:\n self.summary_writer.add_summary(summaries, global_step)\n return loss\n\ndef copy_model_parameters(sess, estimator1, estimator2):\n \"\"\"\n Copies the model parameters of one estimator to another.\n\n Args:\n sess: Tensorflow session instance\n estimator1: Estimator to copy the paramters from\n estimator2: Estimator to copy the parameters to\n \"\"\"\n e1_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator1.scope)]\n e1_params = sorted(e1_params, key=lambda v: v.name)\n e2_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator2.scope)]\n e2_params = sorted(e2_params, key=lambda v: v.name)\n\n update_ops = []\n for e1_v, e2_v in zip(e1_params, e2_params):\n op = e2_v.assign(e1_v)\n update_ops.append(op)\n\n sess.run(update_ops)\n\n\ndef make_epsilon_greedy_policy(estimator, nA):\n \"\"\"\n Creates an epsilon-greedy policy based on a given Q-function approximator and epsilon.\n\n Args:\n estimator: An estimator that returns q values for a given state\n nA: Number of actions in the environment.\n\n Returns:\n A function that takes the (sess, observation, epsilon) as an argument and returns\n the probabilities for each action in the form of a numpy array of length nA.\n\n \"\"\"\n def policy_fn(sess, observation, epsilon):\n A = np.ones(nA, dtype=float) * epsilon / nA\n q_values = estimator.predict(sess, np.expand_dims(observation, 0))[0]\n best_action = np.argmax(q_values)\n A[best_action] += (1.0 - epsilon)\n return A\n return policy_fn\n\n\ndef deep_q_learning(sess,\n env,\n q_estimator,\n target_estimator,\n state_processor,\n num_episodes,\n experiment_dir,\n replay_memory_size=500000,\n replay_memory_init_size=50000,\n update_target_estimator_every=10000,\n discount_factor=0.99,\n epsilon_start=1.0,\n epsilon_end=0.1,\n epsilon_decay_steps=500000,\n batch_size=32,\n record_video_every=50):\n \"\"\"\n Q-Learning algorithm for off-policy TD control using Function Approximation.\n Finds the optimal greedy policy while following an epsilon-greedy policy.\n\n Args:\n sess: Tensorflow Session object\n env: OpenAI environment\n q_estimator: Estimator object used for the q values\n target_estimator: Estimator object used for the targets\n state_processor: A StateProcessor object\n num_episodes: Number of episodes to run for\n experiment_dir: Directory to save Tensorflow summaries in\n replay_memory_size: Size of the replay memory\n replay_memory_init_size: Number of random experiences to sampel when initializing \n the reply memory.\n update_target_estimator_every: Copy parameters from the Q estimator to the \n target estimator every N steps\n discount_factor: Gamma discount factor\n epsilon_start: Chance to sample a random action when taking an action.\n Epsilon is decayed over time and this is the start value\n epsilon_end: The final minimum value of epsilon after decaying is done\n epsilon_decay_steps: Number of steps to decay epsilon over\n batch_size: Size of batches to sample from the replay memory\n record_video_every: Record a video every N episodes\n\n Returns:\n An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\n \"\"\"\n\n Transition = namedtuple(\"Transition\", [\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n\n # The replay memory\n replay_memory = []\n\n # Keeps track of useful statistics\n stats = plotting.EpisodeStats(\n episode_lengths=np.zeros(num_episodes),\n episode_rewards=np.zeros(num_episodes))\n\n # Create directories for checkpoints and summaries\n checkpoint_dir = os.path.join(experiment_dir, \"checkpoints\")\n checkpoint_path = os.path.join(checkpoint_dir, \"model\")\n monitor_path = os.path.join(experiment_dir, \"monitor\")\n\n if not os.path.exists(checkpoint_dir):\n os.makedirs(checkpoint_dir)\n if not os.path.exists(monitor_path):\n os.makedirs(monitor_path)\n\n saver = tf.train.Saver()\n # Load a previous checkpoint if we find one\n latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)\n if latest_checkpoint:\n print(\"Loading model checkpoint {}...\\n\".format(latest_checkpoint))\n saver.restore(sess, latest_checkpoint)\n\n total_t = sess.run(tf.contrib.framework.get_global_step())\n\n # The epsilon decay schedule\n epsilons = np.linspace(epsilon_start, epsilon_end, epsilon_decay_steps)\n\n # The policy we're following\n policy = make_epsilon_greedy_policy(\n q_estimator,\n len(VALID_ACTIONS))\n\n # Populate the replay memory with initial experience\n print(\"Populating replay memory...\")\n state = env.reset()\n state = state_processor.process(sess, state)\n state = np.stack([state] * 4, axis=2)\n for i in range(replay_memory_init_size):\n action_probs = policy(sess, state, epsilons[min(total_t, epsilon_decay_steps-1)])\n action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\n next_state, reward, done, _ = env.step(VALID_ACTIONS[action])\n next_state = state_processor.process(sess, next_state)\n next_state = np.append(state[:,:,1:], np.expand_dims(next_state, 2), axis=2)\n replay_memory.append(Transition(state, action, reward, next_state, done))\n if done:\n state = env.reset()\n state = state_processor.process(sess, state)\n state = np.stack([state] * 4, axis=2)\n else:\n state = next_state\n\n # Record videos\n # Use the gym env Monitor wrapper\n env = Monitor(env,\n directory=monitor_path,\n resume=True,\n video_callable=lambda count: count % record_video_every ==0)\n\n for i_episode in range(num_episodes):\n\n # Save the current checkpoint\n saver.save(tf.get_default_session(), checkpoint_path)\n\n # Reset the environment\n state = env.reset()\n state = state_processor.process(sess, state)\n state = np.stack([state] * 4, axis=2)\n loss = None\n\n # One step in the environment\n for t in itertools.count():\n\n # Epsilon for this time step\n epsilon = epsilons[min(total_t, epsilon_decay_steps-1)]\n\n # Add epsilon to Tensorboard\n episode_summary = tf.Summary()\n episode_summary.value.add(simple_value=epsilon, tag=\"epsilon\")\n q_estimator.summary_writer.add_summary(episode_summary, total_t)\n\n # Maybe update the target estimator\n if total_t % update_target_estimator_every == 0:\n copy_model_parameters(sess, q_estimator, target_estimator)\n print(\"\\nCopied model parameters to target network.\")\n\n # Print out which step we're on, useful for debugging.\n print(\"\\rStep {} ({}) @ Episode {}/{}, loss: {}\".format(\n t, total_t, i_episode + 1, num_episodes, loss), end=\"\")\n sys.stdout.flush()\n\n # Take a step\n action_probs = policy(sess, state, epsilon)\n action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\n next_state, reward, done, _ = env.step(VALID_ACTIONS[action])\n next_state = state_processor.process(sess, next_state)\n next_state = np.append(state[:,:,1:], np.expand_dims(next_state, 2), axis=2)\n\n # If our replay memory is full, pop the first element\n if len(replay_memory) == replay_memory_size:\n replay_memory.pop(0)\n\n # Save transition to replay memory\n replay_memory.append(Transition(state, action, reward, next_state, done)) \n\n # Update statistics\n stats.episode_rewards[i_episode] += reward\n stats.episode_lengths[i_episode] = t\n\n # Sample a minibatch from the replay memory\n samples = random.sample(replay_memory, batch_size)\n states_batch, action_batch, reward_batch, next_states_batch, done_batch = map(np.array, zip(*samples))\n\n # Calculate q values and targets (Double DQN)\n q_values_next = q_estimator.predict(sess, next_states_batch)\n best_actions = np.argmax(q_values_next, axis=1)\n q_values_next_target = target_estimator.predict(sess, next_states_batch)\n targets_batch = reward_batch + np.invert(done_batch).astype(np.float32) * \\\n discount_factor * q_values_next_target[np.arange(batch_size), best_actions]\n\n # Perform gradient descent update\n states_batch = np.array(states_batch)\n loss = q_estimator.update(sess, states_batch, action_batch, targets_batch)\n\n if done:\n break\n\n state = next_state\n total_t += 1\n\n # Add summaries to tensorboard\n episode_summary = tf.Summary()\n episode_summary.value.add(simple_value=stats.episode_rewards[i_episode], node_name=\"episode_reward\", tag=\"episode_reward\")\n episode_summary.value.add(simple_value=stats.episode_lengths[i_episode], node_name=\"episode_length\", tag=\"episode_length\")\n q_estimator.summary_writer.add_summary(episode_summary, total_t)\n q_estimator.summary_writer.flush()\n\n yield total_t, plotting.EpisodeStats(\n episode_lengths=stats.episode_lengths[:i_episode+1],\n episode_rewards=stats.episode_rewards[:i_episode+1])\n\n env.monitor.close()\n return stats\n\n\ntf.reset_default_graph()\n\n# Where we save our checkpoints and graphs\nexperiment_dir = os.path.abspath(\"./experiments/{}\".format(env.spec.id))\n\n# Create a glboal step variable\nglobal_step = tf.Variable(0, name='global_step', trainable=False)\n\n# Create estimators\nq_estimator = Estimator(scope=\"q\", summaries_dir=experiment_dir)\ntarget_estimator = Estimator(scope=\"target_q\")\n\n# State processor\nstate_processor = StateProcessor()\n\nwith tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n for t, stats in deep_q_learning(sess,\n env,\n q_estimator=q_estimator,\n target_estimator=target_estimator,\n state_processor=state_processor,\n experiment_dir=experiment_dir,\n num_episodes=10000,\n replay_memory_size=500000,\n replay_memory_init_size=50000,\n update_target_estimator_every=10000,\n epsilon_start=1.0,\n epsilon_end=0.1,\n epsilon_decay_steps=500000,\n discount_factor=0.99,\n batch_size=32):\n\n print(\"\\nEpisode Reward: {}\".format(stats.episode_rewards[-1]))\n\n" }, { "path": "FA/MountainCar Playground.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import numpy as np\\n\",\n \"from matplotlib import pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2016-09-12 09:17:55,691] Making new env: MountainCar-v0\\n\"\n ]\n }\n ],\n \"source\": [\n \"env = gym.envs.make(\\\"MountainCar-v0\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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m9mqcCLQDJwHJjunHvGzC4AXgNaAtuAwc65/d57ngH6AN8CQ5xzq8vZ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\\n\",\n 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maWDrwOpAAngJedc38ws/OBvwJNgV3A3c65g957/gD0BL4F+jnn1pez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\\nEkf+P4V/x10kXmYCAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"env.reset()\\n\",\n \"plt.figure()\\n\",\n \"plt.imshow(env.render(mode='rgb_array'))\\n\",\n \"\\n\",\n \"[env.step(0) for x in range(10000)]\\n\",\n \"plt.figure()\\n\",\n \"plt.imshow(env.render(mode='rgb_array'))\\n\",\n \"\\n\",\n \"env.close()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "FA/Q-Learning with Value Function Approximation Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import itertools\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"import sklearn.pipeline\\n\",\n \"import sklearn.preprocessing\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"\\n\",\n \"from lib import plotting\\n\",\n \"from sklearn.linear_model import SGDRegressor\\n\",\n \"from sklearn.kernel_approximation import RBFSampler\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2016-11-06 15:54:37,301] Making new env: MountainCar-v0\\n\"\n ]\n }\n ],\n \"source\": [\n \"env = gym.envs.make(\\\"MountainCar-v0\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"FeatureUnion(n_jobs=1,\\n\",\n \" transformer_list=[('rbf1', RBFSampler(gamma=5.0, n_components=100, random_state=None)), ('rbf2', RBFSampler(gamma=2.0, n_components=100, random_state=None)), ('rbf3', RBFSampler(gamma=1.0, n_components=100, random_state=None)), ('rbf4', RBFSampler(gamma=0.5, n_components=100, random_state=None))],\\n\",\n \" transformer_weights=None)\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Feature Preprocessing: Normalize to zero mean and unit variance\\n\",\n \"# We use a few samples from the observation space to do this\\n\",\n \"observation_examples = np.array([env.observation_space.sample() for x in range(10000)])\\n\",\n \"scaler = sklearn.preprocessing.StandardScaler()\\n\",\n \"scaler.fit(observation_examples)\\n\",\n \"\\n\",\n \"# Used to convert a state to a featurizes represenation.\\n\",\n \"# We use RBF kernels with different variances to cover different parts of the space\\n\",\n \"featurizer = sklearn.pipeline.FeatureUnion([\\n\",\n \" (\\\"rbf1\\\", RBFSampler(gamma=5.0, n_components=100)),\\n\",\n \" (\\\"rbf2\\\", RBFSampler(gamma=2.0, n_components=100)),\\n\",\n \" (\\\"rbf3\\\", RBFSampler(gamma=1.0, n_components=100)),\\n\",\n \" (\\\"rbf4\\\", RBFSampler(gamma=0.5, n_components=100))\\n\",\n \" ])\\n\",\n \"featurizer.fit(scaler.transform(observation_examples))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Estimator():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Value Function approximator. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def __init__(self):\\n\",\n \" # We create a separate model for each action in the environment's\\n\",\n \" # action space. Alternatively we could somehow encode the action\\n\",\n \" # into the features, but this way it's easier to code up.\\n\",\n \" self.models = []\\n\",\n \" for _ in range(env.action_space.n):\\n\",\n \" model = SGDRegressor(learning_rate=\\\"constant\\\")\\n\",\n \" # We need to call partial_fit once to initialize the model\\n\",\n \" # or we get a NotFittedError when trying to make a prediction\\n\",\n \" # This is quite hacky.\\n\",\n \" model.partial_fit([self.featurize_state(env.reset())], [0])\\n\",\n \" self.models.append(model)\\n\",\n \" \\n\",\n \" def featurize_state(self, state):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Returns the featurized representation for a state.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" scaled = scaler.transform([state])\\n\",\n \" featurized = featurizer.transform(scaled)\\n\",\n \" return featurized[0]\\n\",\n \" \\n\",\n \" def predict(self, s, a=None):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Makes value function predictions.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" s: state to make a prediction for\\n\",\n \" a: (Optional) action to make a prediction for\\n\",\n \" \\n\",\n \" Returns\\n\",\n \" If an action a is given this returns a single number as the prediction.\\n\",\n \" If no action is given this returns a vector or predictions for all actions\\n\",\n \" in the environment where pred[i] is the prediction for action i.\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" features = self.featurize_state(s)\\n\",\n \" if not a:\\n\",\n \" return np.array([m.predict([features])[0] for m in self.models])\\n\",\n \" else:\\n\",\n \" return self.models[a].predict([features])[0]\\n\",\n \" \\n\",\n \" def update(self, s, a, y):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Updates the estimator parameters for a given state and action towards\\n\",\n \" the target y.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" features = self.featurize_state(s)\\n\",\n \" self.models[a].partial_fit([features], [y])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def make_epsilon_greedy_policy(estimator, epsilon, nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates an epsilon-greedy policy based on a given Q-function approximator and epsilon.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" estimator: An estimator that returns q values for a given state\\n\",\n \" epsilon: The probability to select a random action . float between 0 and 1.\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A function that takes the observation as an argument and returns\\n\",\n \" the probabilities for each action in the form of a numpy array of length nA.\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def policy_fn(observation):\\n\",\n \" A = np.ones(nA, dtype=float) * epsilon / nA\\n\",\n \" q_values = estimator.predict(observation)\\n\",\n \" best_action = np.argmax(q_values)\\n\",\n \" A[best_action] += (1.0 - epsilon)\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def q_learning(env, estimator, num_episodes, discount_factor=1.0, epsilon=0.1, epsilon_decay=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Q-Learning algorithm for fff-policy TD control using Function Approximation.\\n\",\n \" Finds the optimal greedy policy while following an epsilon-greedy policy.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI environment.\\n\",\n \" estimator: Action-Value function estimator\\n\",\n \" num_episodes: Number of episodes to run for.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" epsilon: Chance the sample a random action. Float betwen 0 and 1.\\n\",\n \" epsilon_decay: Each episode, epsilon is decayed by this factor\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Keeps track of useful statistics\\n\",\n \" stats = plotting.EpisodeStats(\\n\",\n \" episode_lengths=np.zeros(num_episodes),\\n\",\n \" episode_rewards=np.zeros(num_episodes)) \\n\",\n \" \\n\",\n \" for i_episode in range(num_episodes):\\n\",\n \" \\n\",\n \" # The policy we're following\\n\",\n \" policy = make_epsilon_greedy_policy(\\n\",\n \" estimator, epsilon * epsilon_decay**i_episode, env.action_space.n)\\n\",\n \" \\n\",\n \" # Print out which episode we're on, useful for debugging.\\n\",\n \" # Also print reward for last episode\\n\",\n \" last_reward = stats.episode_rewards[i_episode - 1]\\n\",\n \" sys.stdout.flush()\\n\",\n \" \\n\",\n \" # Reset the environment and pick the first action\\n\",\n \" state = env.reset()\\n\",\n \" \\n\",\n \" # Only used for SARSA, not Q-Learning\\n\",\n \" next_action = None\\n\",\n \" \\n\",\n \" # One step in the environment\\n\",\n \" for t in itertools.count():\\n\",\n \" \\n\",\n \" # Choose an action to take\\n\",\n \" # If we're using SARSA we already decided in the previous step\\n\",\n \" if next_action is None:\\n\",\n \" action_probs = policy(state)\\n\",\n \" action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\\n\",\n \" else:\\n\",\n \" action = next_action\\n\",\n \" \\n\",\n \" # Take a step\\n\",\n \" next_state, reward, done, _ = env.step(action)\\n\",\n \" \\n\",\n \" # Update statistics\\n\",\n \" stats.episode_rewards[i_episode] += reward\\n\",\n \" stats.episode_lengths[i_episode] = t\\n\",\n \" \\n\",\n \" # TD Update\\n\",\n \" q_values_next = estimator.predict(next_state)\\n\",\n \" \\n\",\n \" # Use this code for Q-Learning\\n\",\n \" # Q-Value TD Target\\n\",\n \" td_target = reward + discount_factor * np.max(q_values_next)\\n\",\n \" \\n\",\n \" # Use this code for SARSA TD Target for on policy-training:\\n\",\n \" # next_action_probs = policy(next_state)\\n\",\n \" # next_action = np.random.choice(np.arange(len(next_action_probs)), p=next_action_probs) \\n\",\n \" # td_target = reward + discount_factor * q_values_next[next_action]\\n\",\n \" \\n\",\n \" # Update the function approximator using our target\\n\",\n \" estimator.update(state, action, td_target)\\n\",\n \" \\n\",\n \" print(\\\"\\\\rStep {} @ Episode {}/{} ({})\\\".format(t, i_episode + 1, num_episodes, last_reward), end=\\\"\\\")\\n\",\n \" \\n\",\n \" if done:\\n\",\n \" break\\n\",\n \" \\n\",\n \" state = next_state\\n\",\n \" \\n\",\n \" return stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"estimator = Estimator()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Step 110 @ Episode 100/100 (-163.0)\"\n ]\n }\n ],\n \"source\": [\n \"# Note: For the Mountain Car we don't actually need an epsilon > 0.0\\n\",\n \"# because our initial estimate for all states is too \\\"optimistic\\\" which leads\\n\",\n \"# to the exploration of all states.\\n\",\n \"stats = q_learning(env, estimator, 100, epsilon=0.0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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GbOnMm4ceNo3ry5yX6MvbcUW1tb9u7dy+zZs/nhhx8YMmQITz31FGvW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\\nQNBu/H/KEP1vONfcjwAAAABJRU5ErkJggg==\\n\",\n 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4549vvjbX4sKyurQlfnD7O/i39S+/k3tZ//Utv5N7Wf/8rMzGTp0qWe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\\nODOwGzepW9ln5WxE7d+Fyt5hTn4aNxF1IL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fj4ePnx3Xff6S3TqVMnvcbF3bt3R1FRkcGlpnLvvfcehg0bhtDQUEyf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\\nh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6gWT0L1GwXlXMLoWFQILHhERERkJhnp0FfMBS6cgzZ4DFRoVaMj0X1g\\nwSMiIiIAgH75EvRpb0NVrAz17iyoEiWNjkT3iQWPiIiIIInxSJs7FqrR41DPd4FSyuhI9ABY8IiI\\niIo5ORMHff4ElOr0Eq4/2tboOFQEWPCIiIiKMTm0F/qq+dBeGgCXlk/iOi8P5xBY8IiIiIohEYFs\\njYT8sAHakPegQngwhSNhwSMiIipmRNchEcsgfxyBNnoalF9poyNREWPBIyIiKkZEz4Gsmg+J/xva\\nyMlQbu5GRyILYMEjIiIqJiQ7C7JsFuRqKrSh46BcXI2ORBbCgkdERFQMyPVM6IumApoGbfC7PMed\\ng2PBIyIicnCSkQ59/kQobz+onkOgnPm/f0fH7zAREZEDk6tp0Oe8D/VQJahu/aA0zehIZAUseERE\\nRA5KdB36splQIVWgurzKq1MUI6zxREREDkq+jQAy0qHCe7PcFTMseERERA5IDu+F7NwC7dVR3Oeu\\nGOJ3nIiIyMHo+3dBPl8CbfAYKG9fo+OQAVjwiIiIHIi+aytkw2fQho2DCg41Og4ZhAWPiIjIQeg/\\nfA354WtoIydBlQkyOg4ZiAWPiIjIAeibv8zd527kJCj/QKPjkMFY8IiIiOycvnENZO+O3GvL+vob\\nHYdsAAseERGRHdM3roHs25W7cscDKugfPE0KERGRndK3rof8vB3a8Aksd5QHV/CIiIjskL59EyRq\\nE1fuKF8seERERHZERCBbvsotdyMmQvmVNjoS2SAWPCIiIjshug6JWAaJ+R3amx/wgAq6IxY8IiIi\\nOyBZWZAVsyGXTdDemAzl5mF0JLJhLHhEREQ2TtLToC+YBHh4QXt9LFSJkkZHIhvHgkdERGTDxJQI\\nfc5YqJp1ocJfgdKcjI5EdoAFj4iIyEbJX6egzxsP1eY5qPYdoZQyOhLZCRY8IiIiGyRHDkBfMQeq\\ncx9oTVoYHYfsDAseERGRjdG3bYRsWgdt4NtQlWsYHYfsEAseERGRDdE3rYPs3Q5t1FSo0mWNjkN2\\nigWPiIjIRkjsUci2jdDemQnlw3Pc0f3jtWiJiIhsgKRdgb5sJrTur7Hc0QNjwSMiIjKYXEuHvmAS\\nVOPHoWo3NDoOOQAWPCIiIgNJehr0WWOggipAdepudBxyENwHj4iIyCByNRX6rPegKteA6tyH57mj\\nIsMVPCIiIgOICPRls6AqVWe5oyLHgkdERGQA+fEbIO0KVHgvljsqcix4REREViZnT0C+jYDWZwSU\\nM/eWoqLHgkdERGRFkpEOffG03LdleSJjshCb+LVh48aNiIqKglIKFSpUwIABA2AymTBnzhykpaUh\\nNDQUgwcPhpOTk9FRiYiIHoh8thiqai1oj7Q0Ogo5MMNX8EwmEzZv3oypU6di+vTpyMnJwU8//YTV\\nq1fj2WefxZw5c+Du7o5t27YZHZWIiOiB6D9HQU79CdWlr9FRyMEZXvAAQNd1ZGRkICcnB9evX4ef\\nnx+io6PxyCOPAABatmyJffv2GZySiIjo/smFc5CIZdBeHQnl4mp0HHJwhr9F6+fnh2effRYDBgyA\\ni4sL6tSpg9DQULi7u0PTcvunv78/Ll26ZHBSIiKi+yOZGdAXTYXq9DJUcKjRcagYMHwF7+rVqzhw\\n4AA+/PBDLF68GJmZmTh06NBt9+Mh5EREZI9Ez4GsmANVoTLUY+2MjkPFhOEreEeOHEFgYCA8PDwA\\nAE2aNEFsbCyuXr0KXdehaRqSk5Ph6+ub7+dHR0cjOjrafDs8PByenp5WyU65SpYsyZlbGWdufZy5\\n9TnCzEXXcW3JNOiZ1+A+ZAxUyZJGR7orR5i5PYqIiDB/HBYWhrCwsAd+TMMLXkBAAP78809cv34d\\nJUqUwJEjR1C5cmWEhYVh7969aNasGXbs2IFGjRrl+/n5DSI1NdUa0ekfnp6enLmVcebWx5lbn73P\\nXEQga5ZC/joD7fWxSMvMBDIzjY51V/Y+c3vk6emJ8PDwIn9cwwtelSpV0LRpU4waNQpOTk4ICQnB\\nE088gQYNGmD27NlYu3YtQkJC0KZNG6OjEhERFYhkZ0FWL4KcOwVt2HgeVEFWp0REjA5R1M6fP290\\nhGKFv/FZH2dufZy59dnrzOVqGvRFUwAXV2i9h0O5ljI6UoHZ68ztWVBQkEUe1/AVPCIiIkchCReg\\nzxsP9XBDqBd7QGk8QT8ZgwWPiIioCEhsNPTFU6Ge6wyt1dNGx6FijgWPiIjoAel7tkG+WAGt1zCo\\nsPpGxyFiwSMiIrpfIgL5+jPI3u3QRkyECqpgdCQiACx4RERE90VEIGs/gvwZDW30NCgvH6MjEZmx\\n4BERERWS6Drks0WQsyehDZ8A5eZhdCSiPFjwiIiICkF0HbJqPuTieWhDx0GVcjM6EtFtWPCIiIgK\\nSHQd8umHkMQL0F5/nycwJpulGR2AiIjIHoiu516d4sI5aIPHsNyRTeMKHhER0T1Iehpk1QJISnLu\\nyp0dXZ2Ciieu4BEREd2FxEZDH/c64OWTe11ZV+5zR7aPK3hERET5kJwcyMY1kF1bob00CKpuY6Mj\\nERUYCx4REdEtJOMa9MUfADnZ0N6ZBeXjZ3QkokJhwSMiIrqJXLkEfe54qOAQqP8OgHLm/yrJ/vBV\\nS0RE9A+5eB76nPehmraCeq4LlFJGRyK6Lyx4RERE+OdgisVToTr+F9rj7Y2OQ/RAWPCIiKjY03/Z\\nAVn7EbTew6Bq1Tc6DtEDY8EjIqJiS/QcyMa1kN0/5l5TtnxFoyMRFQkWPCIiKpYkxQR92UxAz4E2\\nehqPlCWHwoJHRETFjhz9FfrKuVAt/g/q2XAozcnoSERFigWPiIiKFX3zl5Bt30LrMxKq+sNGxyGy\\nCBY8IiIqNvTt30F2boH21jQoH3+j4xBZDAseEREVC3LgJ8i3a6GNnMxyRw6PBY+IiBye+TQoQ8dB\\nBZYzOg6RxbHgERGRwxIRyOYvIds3QRuWe/kxouKABY+IiByS5ORAPl8MORED7c1pUL58W5aKDxY8\\nIiJyOHItHfqSDwBdh/bGFKhSbkZHIrIqFjwiInIoknQR+rzxUFVrQXXuC+XM/9VR8cNXPREROQw5\\nFQt9wSSo//sXVNvnoZQyOhKRIVjwiIjIIciJGOgLJkJ7eRBUvUeMjkNkKBY8IiKye+Zy1/N1qNoN\\njY5DZDjN6ABEREQPQk7+wXJHdAsWPCIislty5gT0+ROg9RzCckd0ExY8IiKyS/LXaehzx0L77wCo\\n2o2MjkNkU7gPHhER2R35fT/0VfOhOveBavCo0XGIbA4LHhER2Q25mgpZszT36hS9h0PVqGN0JCKb\\nxIJHRER2QQ7thb56EVSj5tDemwvl4mp0JCKbxYJHREQ2TUQgX38O+WU7tL4joaqFGR2JyOax4BER\\nkc0SEciG1ZBDe6G9+QGUl4/RkYjsAgseERHZJBGBRK6G/PYLtBEToTy9jY5EZDdY8IiIyObI9Uyk\\nL5gHOXsC2vAJLHdEhcTz4BERkU2R5EToU98E9Bxob0xluSO6D1zBIyIimyF/HIW+dDpU+w5w6/QS\\n0tLSjI5EZJdY8IiIyHAiAtnyFeT7DdBeGQoVVh9KKaNjEdktFjwiIjKUpKdBXzEHuHwJ2lszoPxL\\nGx2JyO6x4BERkWHk7Enoi6ZAPdwQ6tU3oJxLGB2JyCGw4BERkSH0n76HfPkxVOc+0B5paXQcIodi\\nEwUvPT0dixYtwrlz56CUQv/+/VGuXDnMnj0biYmJCAwMxNChQ+Hm5mZ0VCIiekByPRPy2WLIyT+g\\njZwEFVTB6EhEDscmCt6KFStQv359DBs2DDk5OcjMzMRXX32F2rVro0OHDoiMjMT69evRrVs3o6MS\\nEdEDkKws6DPfhfIrDe2t6VCupYyOROSQDD8P3rVr1xATE4PWrVsDAJycnODm5oYDBw6gZcvcJftW\\nrVph//79RsYkIqIiIBHLAG9fqD4jWO6ILMjwFbyLFy/C09MTH374Ic6cOYNKlSqhR48euHz5Mnx8\\ncq856OPjgytXrhiclIiIHoS+Nwpy7DC0t2fwFChEFmZ4wdN1HadOnUKvXr1QuXJlrFy5EpGRkQX+\\n/OjoaERHR5tvh4eHw9PT0xJR6Q5KlizJmVsZZ259nPmDyTl3CmkRy+H57kw4lSlboM/hzK2PMzdG\\nRESE+eOwsDCEhYU98GMaXvD8/Pzg7++PypUrAwCaNm2KyMhI+Pj4ICUlxfy3t3f+l6rJbxCpqakW\\nz03/4+npyZlbGWdufZz5/ZOMa9BnjIF6oQfSfUsDBZwjZ259nLn1eXp6Ijw8vMgf1/B98Hx8fODv\\n74/z588DAI4cOYLg4GA0bNgQ27dvBwBs374djRo1MjAlERHdDxGBfLIAqkpNaM3aGh2HqNgwfAUP\\nAHr27Il58+YhOzsbZcqUwYABA6DrOmbNmoWoqCgEBARg2LBhRsckIqJCED0HsnoRJDEe2oiJRsch\\nKlaUiIjRIYrajdVAsg4u6VsfZ259nHnhSHYWZPlsyJUUaIPehnIt/HlMOXPr48ytLygoyCKPaxMr\\neERE5Dgk6zr0hVMATYM25D2oEiWNjkRU7LDgERFRkZGsLOgfToZycYXqPRzKmf+bITKC4QdZEBGR\\nY5CsLOgLJwMlXVjuiAxWqIKXmpqKnTt3YsOGDQAAk8mE5ORkiwQjIiL7IdlZ0Jd8ADg7Q+szguWO\\nyGAFLnjHjh3D66+/jl27duHLL78EAMTHx2Pp0qUWC0dERLZPsrOhL5kGiEDrO5LljsgGFLjgrVy5\\nEq+//jrefvttODk5AQCqVKmCEydOWCwcERHZNsnJgf7RdCA7G9qro6CcSxgdiYhQiIMsEhMTUbt2\\n7byf7OyMnJycIg9FRES2TzLSoX80E8jJhjbgbagSLHdEtqLAK3jBwcE4fPhwnm1HjhxBhQoVijwU\\nERHZNkmMhz5lFJSXD7SBLHdEtqbAK3gvvfQSpk6divr16+P69etYsmQJfv31V4wcOdKS+YiIyMbI\\nH0ehL/kA6ulwqDbPQClldCQiukWhrmRhMpmwa9cuJCYmIiAgAI8//jj8/f0tme++8EoW1sUzn1sf\\nZ259nHkuffePkC9XQus9HKpWPYt+Lc7c+jhz67OJK1n4+fmhQ4cOFglCRES2S0QgG1ZD9u2ENnIy\\nVLlgoyMR0V3cteDNmzevQEvvgwYNKrJARERkWyQ7G7JyDiQxHtqbH0B5+RgdiYju4a4HWZQtWxZl\\nypRBmTJl4Obmhv3790PXdfj5+UHXdezfvx9uboW/gDQREdkPifgIknYF2vAJLHdEduKuK3gvvvii\\n+eOJEyfizTffRM2aNc3bYmJizCc9JiIix6Pv2go5/hu00dOhSroYHYeICqjAp0mJjY1F1apV82yr\\nUqUKYmNjizwUEREZT47/Bln/Se5pUNzcjY5DRIVQ4IIXGhqKzz//HNevXwcAXL9+HWvWrEFISIil\\nshERkQFEBPoPG6B/NAPaq29AleUBFUT2psBH0Q4YMABz585F9+7d4eHhgbS0NFSuXBmvvfaaJfMR\\nEZEVSWYmZNV8yIWz0EZPgwooY3QkIroPBS54gYGBmDBhApKSknDp0iX4+voiICDAktmIiMiK5Mol\\n6HPGQgVVgDbqAygX7nNHZK8KdR68tLQ0REdHw2Qywc/PDw0bNoSHh4elshERkZVISjL0Ge9CNXoM\\n6vkuvDoFkZ0r1EEWgwcPxvfff48zZ87ghx9+wODBg3mQBRGRnZPkROjT3oJq1gZah64sd0QOoMAr\\neCtXrkTv3r3RvHlz87Y9e/ZgxYoVmDx5skXCERGRZUliPPQZ70A98Ry0J3ilIiJHUeAVvAsXLuDR\\nRx/Ns61p06aIj48v8lBERGR5cvE89OlvQf1fJ5Y7IgdT4IJXtmxZ7NmzJ8+2n3/+GWXK8AgrIiJ7\\nIxfOQZ/+NtSznaG1etroOERUxAr8Fm2PHj0wZcoUfPfddwgICEBiYiIuXLiAN99805L5iIioiOl7\\noyBrl0GF94L2aGuj4xCRBRS44FWvXh3z5s3DwYMHcenSJTRs2BANGjTgUbRERHZCrqVDVi+EnDkB\\nbeg4qAqVjI5ERBZSqNOkeHh4oEWLFgCAixcv4tq1ayx4RER2QE7/CX3JNKia9aC9M4vnuCNycAXe\\nB2/27Nn4448/AABRUVEYNmwYhg0bhm3btlksHBERPTg5/hv0ueOg/bsHtJcGsNwRFQMFLnhHjx5F\\n5cqVAQDWonOzAAAgAElEQVQbN27Eu+++i0mTJiEyMtJi4YiI6MHI7/uhL50O7dVRUA2bGR2HiKyk\\nwG/RZmdnw9nZGSaTCWlpaahRowYA4PLlyxYLR0RE90/ftxOyZim0Qe9AVapudBwisqICF7yQkBCs\\nX78eiYmJaNCgAQDAZDKhVKlSFgtHRET3R/9+A+T7DdCGjYcKDjE6DhFZWYHfou3Xrx/Onj2L69ev\\no3PnzgByL1/22GOPWSwcEREVjug69IhlkF1boY2aynJHVEwpERGjQxS18+fPGx2hWPH09ERqaqrR\\nMYoVztz67GHmkpEOfeVc4PKl3Ldl3T2NjvRA7GHmjoYzt76goCCLPO5d36LduXOn+bQodztatk2b\\nNkWbioiICkUunIP+4WSoqrWgeg2DKlHS6EhEZKC7Frzdu3ebC96uXbvueD8WPCIi48iBn6CvXgT1\\n7+7QHmtndBwisgF8i5YeGJf0rY8ztz5bnbm++UvI9u+g9R8NVbGy0XGKlK3O3JFx5tZnyFu0t7p6\\n9ar5UmW+vr5o0KAB3N3dLRKMiIjuTEQg36yB7N+ZezCFr7/RkYjIhhS44B09ehTTp09HUFAQAgIC\\nkJycjGXLlmH48OGoXbu2JTMSEdFNRASyfhXk9wPQRk6C8vI1OhIR2ZgCF7xly5ahb9++aNbsf2dC\\n//nnn7Fs2TLMnj3bIuGIiCgvycyErJoHSbgAbfhEKE8voyMRkQ0q8HnwLl26hKZNm+bZ1qRJE6Sk\\npBR5KCIiup2YEqF/8CYAlbtyx3JHRHdQ4ILXokULbN68Oc+2rVu3mo+yJSIiy5G4Y9AnjYRq8jhU\\n72FQJV2MjkRENqzAb9GeOnUK33//Pb7++mv4+fnBZDLh8uXLqFq1Kt577z3z/caOHWuRoERExZGI\\nQHZ8B/n6c2g9h0DVbmR0JCKyAwUueG3btkXbtm0tmYWIiG4iWdchqxdBTv6Re6RsGcucToGIHM89\\nC97y5cvxyiuvoFWrVgByr2hx84mNp0+fjhEjRlgsIBFRcSSpV6DPGwf4BkB7axqUq5vRkYjIjtxz\\nH7wdO3bkuf3JJ5/kuX3kyJGiTUREVMxJign6tNFQNWpD6zeK5Y6ICu2eK3j3utCFA14Ig4jIMGJK\\nhD7jXahHW0N79j9GxyEiO3XPgqeUeqB/JyKigpHzZ6HPHQfV5hlo7f9ldBwismP3LHg5OTk4evSo\\n+bau67fdJiKiByPHDkP/aAbUCz2hNWtz708gIrqLexY8b29vLFy40Hzbw8Mjz20vr6I50aau6xg9\\nejT8/PwwatQoJCQkYM6cOUhLS0NoaCgGDx4MJyenIvlaRES2RN+1FbL+E2ivjoKq/rDRcYjIAdyz\\n4C1YsMAaObBp0yaUL18e165dAwCsXr0azz77LB599FEsXboU27ZtQ7t27ayShYjIGiTjGmTtR5DY\\naGhvTIEqW97oSETkIAp8JQtLSk5OxqFDh/KcZ+/o0aN45JFHAAAtW7bEvn37jIpHRFTk5EQM9HFD\\nABFo785kuSOiIlXgEx1b0scff4yXXnoJ6enpAIDU1FR4eHhA03L7p7+/Py5dumRkRCKiIiG6Dtm4\\nFrLjO2jd+kE1aGZ0JCJyQIYXvIMHD8Lb2xshISGIjo4G8M+leW45/cqdjtaNjo42fx4AhIeHw9PT\\n03KB6TYlS5bkzK2MM7e+opi5iODastnIOXcS7lOWQvMLKKJ0jomvc+vjzI0RERFh/jgsLAxhYWEP\\n/JiGF7yYmBgcOHAAhw4dwvXr13Ht2jWsXLkS6enp0HUdmqYhOTkZvr6++X5+foNITU21RnT6h6en\\nJ2duZZy59RXFzPUvP4b8eQza8Am4WsIF4Pfwrvg6tz7O3Po8PT0RHh5e5I9reMHr2rUrunbtCgA4\\nduwYvvnmG7z22muYNWsW9u7di2bNmmHHjh1o1IgX2CYi+yTZWZBv1kB+2wftjclQpXhlCiKyLJs4\\nyCI/3bp1w8aNGzFkyBCkpaXluf4tEZE9EBHIr3ugjxkIOXcK2rBxUB5Fc2opIqK7UeKA1xo7f/68\\n0RGKFS7pWx9nbn2Fnbmc+hN6xDIgIx3ai69A1apnwXSOia9z6+PMrS8oKMgij2v4W7RERI5EMq5B\\n1iyBRB+C6tANqlkbKI0naSci62LBIyIqIvL3GeiLpkJVrgFt/EIo11JGRyKiYooFj4ioCOh7foSs\\nWwH1Yk9ozdre+xOIiCyIBY+I6AFIdhbk8yWQ2KPQRkyEKl/R6EhERCx4RET3S66kQF84BXD3gPbW\\nDJ7+hIhsBgseEdF9kLMnoC+YlHsQxXNdoDSbPesUERVDLHhERIUgV9MgG9dA9kZB69YfqtFjRkci\\nIroNCx4RUQFIdjb0H7+BfBsB1eBRaGMXQHn5GB2LiChfLHhERPcgR35F6rrlEN8AaMMn8EAKIrJ5\\nLHhERHcgOTmQr1ZBft0N997DkFGlltGRiIgKhAWPiCgfkmKCvnQaUKIktHdmokS58sjgJZyIyE6w\\n4BER3UJO/Qn9w4lQjz8J9Ww4LzVGRHaHBY+I6CZy9iT0eeOgvTwQql5To+MQEd0XnriJiOgfcuEc\\n9LljoXV9leWOiOwaV/CIqNgTEeD3/dA/XQjV6WWe246I7B4LHhEVa3LhL+hrlwLJCdC6D4Z6uIHR\\nkYiIHhgLHhEVS5KeBvlmLWRvFNTTL0K1fgbKmT8Sicgx8KcZERU7cnAP9M+WQNVpBG3sfF6Rgogc\\nDgseERUbknoF8vliyJkT0Pq9AcUTFxORg2LBI6JiQX7dA/3zxVCPtITW/TUoFxejIxERWQwLHhE5\\nNBGBfBsB2fMjtH5vQlWpaXQkIiKLY8EjIoclIpB1yyHHf4M2aiqUt6/RkYiIrIIFj4gcklzPhHy2\\nGHLhHLQRk6DcPYyORERkNbySBRE5HPltH/T3BgGZGdCGjmO5I6Jihyt4ROQwJOEC9DVLgcQL0F4a\\nCFWrntGRiIgMwYJHRHZPRCBb10M2fwnVvhPUgNFQziWMjkVEZBgWPCKya6LrkDVLIX8eg/bObCj/\\n0kZHIiIyHAseEdktyc6CrJgDSUmGNnISlJu70ZGIiGwCCx4R2SXJyoK+cDKgadCGvA9VkicuJiK6\\ngQWPiOyOZGdBXzwVKFESWp8RUM78UUZEdDOeJoWI7IpkZ0NfMg0AoPUZznJHRJQP/mQkIrshcceh\\nr/0I8PLJvewYj5QlIsoXCx4R2TxJToB8+TEk7jhUp5egmrSE0vgGBBHRnbDgEZHNkswMyKZ1kB2b\\nodo8A637YCgXV6NjERHZPBY8IrJJknYF+txxgF8AtDFzoPwCjI5ERGQ3WPCIyOaIKQn67Peg6jSG\\n+nd3KKWMjkREZFdY8IjIpkj837nlrvXT0J7sZHQcIiK7xIJHRDZDzp6APnc8VMdu0B5rZ3QcIiK7\\nxYJHRDZBYqOhL5oCrVt/qIbNjI5DRGTXWPCIyHBy5Ffoy2flnri4Vn2j4xAR2T0WPCIylL5/F+Tz\\nJdAGvQNVuYbRcYiIHAILHhEZRt+5BfLN59CGjYMKDjU6DhGRw2DBIyKrExHIlq8g27+DNnISVGCQ\\n0ZGIiBwKCx4RWZXoOuSLFZBjh6GNmgrl6290JCIih8OCR0RWI9nZkI/nQpIuQhs5Gcrdw+hIREQO\\niQWPiKxCMq5BXzwVcHKG9vo4KBcXoyMRETkswwtecnIy5s+fj5SUFGiahrZt2+Lpp59GWloaZs+e\\njcTERAQGBmLo0KFwc3MzOi4R3QdJvQx97jio8hWhXhoI5eRkdCQiIodmeMFzcnJC9+7dERISgoyM\\nDIwaNQp169ZFVFQUateujQ4dOiAyMhLr169Ht27djI5LRIUkifHQ54yFatgcqmM3XleWiMgKNKMD\\n+Pj4ICQkBADg6uqK8uXLIzk5GQcOHEDLli0BAK1atcL+/fsNTElE90N+2w998kiots9B+9d/We6I\\niKzE8BW8myUkJODMmTOoVq0aLl++DB8fHwC5JfDKlSsGpyOigpKcHMj6TyD7d0Ib8BZUlZpGRyIi\\nKlZspuBlZGRg5syZ6NGjB1xdXY2OQ0T3SRLjoa+YDbi4QntnNpSnl9GRiIiKHZsoeDk5OZgxYwZa\\ntGiBxo0bA8hdtUtJSTH/7e3tne/nRkdHIzo62nw7PDwcnp6eVslNuUqWLMmZW5ktzlxycpC5aR0y\\nv/4crs93gcsz4VCa4XuBFBlbnLmj48ytjzM3RkREhPnjsLAwhIWFPfBjKhGRB36UBzR//nx4enqi\\ne/fu5m2ffvopPDw80LFjR0RGRuLq1asFPsji/PnzlopK+fD09ERqaqrRMYoVW5u5nPoT+ifzAU9v\\naN36QwWWMzpSkbO1mRcHnLn1cebWFxRkmSv5GL6CFxMTg127dqFChQp44403oJRCly5d0LFjR8ya\\nNQtRUVEICAjAsGHDjI5KRLcQXYdsWA356XuoF1+BeqQlD6QgIrIBNrGCV9S4gmdd/I3P+mxh5pKV\\nBVkxG3IpCdqAtx1+XztbmHlxw5lbH2dufQ67gkdE9kfS06B/OBlw94A2dBxUSV6VgojIlrDgEVGh\\nSPzf0D+cBFWjDlTn3lAar0pBRGRrWPCIqMDk4M/QP/0w94oUjz/J/e2IiGwUCx4R3ZPoOZDITyG/\\n7IQ2eAxUaFWjIxER0V2w4BHRXcmVFOhLpwNKQXtnJpRn/uekJCIi28GCR0R3JHHHoS+ZBvVoG6gO\\nXbi/HRGRnWDBI6LbiJ4D+XEj5LsvoHV/DapuY6MjERFRIbDgEVEeEvM79LXLANdS0EZPgypd1uhI\\nRERUSCx4RAQAkITz0NetBM6dhPZCD6Bhcx4lS0Rkp1jwiIo5SYyHbNsI2RsF1f5fUH1HQJUoaXQs\\nIiJ6ACx4RMWQ6DlA9CHoUZuAU39APdoG2vvzobx9jY5GRERFgAWPqBiR1CuQ3d9DdmwG3D2hWj8N\\n1W8ULzVGRORgWPCIigG5mpp7ouJ9O6HqNYXW9w2erJiIyIGx4BE5MBGB/BwF+XIlVINm0CYshvL0\\nMjoWERFZGAsekYOS82ehr14EZFyDNuhdrtgRERUjLHhEDkYS4yHffQE59DPUs12gWj/FK1AQERUz\\nLHhEDkLi/4ZsWgc5sh+q5VPQxi+E8uDbsURExRELHpGdk/NnId9GQI4dhmrzLLSJi6HcPIyORURE\\nBmLBI7JTcvE85JvPc4tduw7Q/jsAqpSb0bGIiMgGsOAR2RlJTkD6Z4ug7/8J6onnof23P5Qrix0R\\nEf0PCx6RnZDLl3Lfit23E6p9B2gTFkG5861YIiK6HQsekY2TjHTIlkhI1LdQj7aGNm4BSpV/CNmp\\nqUZHIyIiG8WCR2SjJDsbsmsr5Nu1UDXqQHtnJlRAGaNjERGRHWDBI7Ixci0dsn8XZMt6ICAQ2mtj\\noCpUNjoWERHZERY8IhsgIsCfxyA/fQ85/AtQo3buwRM16xodjYiI7BALHpGBJMWUe63Y3T8ASkE9\\n1g7aC92hvHyNjkZERHaMBY/IykQEiPkd+rZvgdgjUA2aQes5BKhUHUopo+MREZEDYMEjshLJzID8\\nsh3y40ZABKrtc1C9hkK5ljI6GhERORgWPCILk6SLkKhNkD0/AFVqQevcB6hRh6t1RERkMSx4RBYi\\nV9MgG1ZD9u+EatYW2lszoEqXNToWEREVAyx4REVMdB2y+wdI5KdQDR6FNn4hlIeX0bGIiKgYYcEj\\nKkJyIgb6mqWAkxO0196Dqsjz1xERkfWx4BEVAYk7Bv3bCOCvM1D/egnq0dbcx46IiAzDgkd0n8yn\\nO/k2AkhOgHrq31AD3oYqUcLoaEREVMyx4BEVklxNhRw9CNm2EUhPg3o6HKpJCygnJ6OjERERAWDB\\nI7on0XXg3EnIkV8hR38F/j4DVHsY6okOUA0fhdJY7IiIyLaw4BHlQ66mQY4dAo78Cok+CLi5Qz3c\\nENrzXYCqYVAlShodkYiI6I5Y8Ij+ISkmyP5dkIN7gL9O5xa52g2hPdeZ568jIiK7woJHxZpkpEMO\\n/gz5ZQdw+k+oek2hPROe+xZsSRej4xEREd0XFjwqdiQ7G4g+lHtd2KO/5pa5x9pDDXybpY6IiBwC\\nCx4VC5J0EXL8N+D4b7l/ly0P9UhLaF1ehfLkVSaIiMixsOCRQ5LUK5CY34GYfwpdxjWomnWBmnWh\\n/bsHlH9poyMSERFZDAseOQTJuAbEHYPcWKFLjM89SKJmXWitnwHKV+SVJYiIqNhgwSO7Izk5wPmz\\nkFOxwOk/c/9OOA9UrAJVsx60rq8CIdWgnPnyJiKi4on/BySbJiJA0sXcEnfqT8jpWODcKcA3ACq0\\nKhBaDdrj7YHgUF4ijIiI6B8seGQzJDsLSE4E4v+GnPkTcupP4HQs4FwSCK0KFVoN2vNdc1fq3NyN\\njktERGSzWPDIquR6JpB4EUg8D0mIBxIvQBIuAAkXgJRkwDcACCwHVbEKtJZPAt0HQfn4Gx2biIjI\\nrth0wTt8+DBWrlwJEUHr1q3RsWNHoyNRAUhGOnBLeZPE+NwSl3oZCAgESpeDCiwHlHsIWt0mQOly\\ngH9pKGe+zUpERPSgbLbg6bqOZcuWYcyYMfD19cXo0aPRuHFjlC9f3uhoBECupuYWt4QLyLhsgv7X\\n6f+txGVmAKXL5q7ElS4HhFSF1qQFEFgO8PWH0pyMjk9EROTQbLbgxcXFoVy5cihdOvd8Zc2bN8f+\\n/ftZ8KxERIDLl/Kuvt1YkUu8AAj+KXBlIeUrANVrQ3usXW6J8/bjKUmIiIgMZLMFz2Qywd//f/te\\n+fn5IS4uzsBEjkf0HMCUlKfESeI/q3CJ8YCL6/9W4QLLAXUaQwssl/t2qoenucSV8vREdmqqwc+G\\niIiIbrDZgpcfrgo9OMnOgv7h5NwSl5wAeHrnlrjAckDpstAqVct9e7V0OahSbkbHJSIiovtgswXP\\nz88PSUlJ5tsmkwm+vr633S86OhrR0dHm2+Hh4QgKCrJKRrs1ZVGRP6Snp2eRPybdHWdufZy59XHm\\n1seZW19ERIT547CwMISFhT3wY2oP/AgWUqVKFcTHxyMxMRHZ2dnYvXs3GjVqdNv9wsLCEB4ebv5z\\n85DIOjhz6+PMrY8ztz7O3Po4c+uLiIjI02OKotwBNryCp2kaevXqhQkTJkBE0KZNGwQHBxsdi4iI\\niMjm2WzBA4B69ephzpw5RscgIiIisis2+xbt/SqqpU0qOM7c+jhz6+PMrY8ztz7O3PosNXMlImKR\\nRyYiIiIiQzjcCh4RERFRcceCR0RERORgbPogi8I6fPgwVq5cCRFB69at0bFjR6Mj2a2BAwfCzc0N\\nSik4OTlh8uTJSEtLw+zZs5GYmIjAwEAMHToUbm65J0Nevnw5Dh8+DBcXFwwcOBAhISEAgO3bt2P9\\n+vUAgE6dOqFly5ZGPSWbtHDhQhw8eBDe3t6YPn06ABTpnE+ePIkPP/wQWVlZqF+/Pnr06GH152hL\\n8pv3unXr8OOPP8Lb2xsA0KVLF9SrVw8AsH79ekRFRcHJyQk9evRA3bp1Adz5Z01CQgLmzJmDtLQ0\\nhIaGYvDgwXByKt7XXk5OTsb8+fORkpICTdPQtm1bPP3003ydW9CtM3/iiSfw1FNP8bVuQVlZWXjv\\nvfeQnZ2NnJwcNG3aFC+++OId55SdnY358+fj5MmT8PT0xNChQxEQEACg8N+LOxIHkZOTI4MGDZKE\\nhATJysqSESNGyF9//WV0LLs1cOBASU1NzbPtk08+kcjISBERWb9+vXz66aciInLw4EGZNGmSiIjE\\nxsbKW2+9JSIiqampMmjQILl69aqkpaWZP6b/OX78uJw6dUqGDx9u3laUcx49erT8+eefIiIyadIk\\nOXTokNWemy3Kb94RERHyzTff3Hbfc+fOyciRIyU7O1suXrwogwYNEl3X7/qzZubMmbJnzx4REVmy\\nZIls3brVOk/Mhl26dElOnTolIiLXrl2T1157Tf766y++zi3oTjPna92yMjIyRCS3j7z11lsSGxt7\\nxzlt2bJFli5dKiIiu3fvllmzZonI/X0v7sRh3qKNi4tDuXLlULp0aTg7O6N58+bYv3+/0bHslohA\\nbjn+5sCBA+bfmFu1aoUDBw4AAPbv32/eXrVqVaSnpyMlJQW//fYb6tSpAzc3N7i7u6NOnTo4fPiw\\ndZ+IjatRowbc3d3zbCuqOaekpODatWuoUqUKAKBFixbF/r+J/OYN4LbXOpD7fWjWrBmcnJwQGBiI\\ncuXKIS4u7q4/a44ePYpHHnkEANCyZUvs27fPsk/IDvj4+JhX4FxdXVG+fHkkJyfzdW5B+c3cZDIB\\n4GvdklxcXADkrubl5ORAKYXo6Og8c7oxv5tf502bNsXRo0cB3N/34k4c5i1ak8kEf39/820/Pz/E\\nxcUZmMi+KaUwceJEKKXwxBNPoG3btrh8+TJ8fHwA5P4AuXz5MoD8Z28yme64ne6uqOZ863Z/f3/O\\n/w62bNmCnTt3onLlynj55Zfh5uYGk8mEatWqme9zY64iku/PmtTUVHh4eEDTcn9v9vf3x6VLl6z+\\nXGxZQkICzpw5g2rVqvF1biU3Zl61alXExMTwtW5Buq7jzTffxMWLF/Hkk0+iTJkycHd3zzOnG6/N\\nm1+3mqbBzc0NaWlphf5e3I3DFLz8KKWMjmC3JkyYAB8fH1y5cgUTJkwo9PV9lVL5/qZIRetuc85v\\nO/+buN2TTz6JF154AUoprFmzBqtWrUK/fv3uOL+7bb/13zjv/8nIyMDMmTPRo0cPuLq6Fupz+Tq/\\nP7fOnK91y9I0DR988AHS09Mxffp0/P3337fd515zKuz34q557pHXbvj5+SEpKcl822QywdfX18BE\\n9u3Gb9ZeXl5o3Lgx4uLi4OPjg5SUFABASkqKeUddPz8/JCcnmz83OTkZvr6+8Pf3z/M9SU5Ohp+f\\nnxWfhX0qqjn7+/vne3/Ky8vLy/yDsm3btubfivObq6+v7x1/1nh5eeHq1avQdT3P/QnIycnBjBkz\\n0KJFCzRu3BgAX+eWlt/M+Vq3Djc3N9SqVQuxsbF3nNPNr3Nd15Geng4PD49Cfy/uxmEKXpUqVRAf\\nH4/ExERkZ2dj9+7daNSokdGx7FJmZiYyMjIA5P4G+Pvvv6NChQpo2LAhtm/fDiD3aLYb823UqBF2\\n7NgBAIiNjYW7uzt8fHxQt25dHDlyBOnp6UhLS8ORI0fMRwPR/9z623BRzdnHxwelSpVCXFwcRAQ7\\nd+40/6Avzm6d942SAQC//PILHnroIQC5896zZw+ys7ORkJCA+Ph4VKlSJd+fNTfm+vDDD2Pv3r0A\\ngB07dvBn0D8WLlyI4OBgPP300+ZtfJ1bVn4z52vdcq5cuYL09HQAwPXr13HkyBEEBwcjLCws3znd\\n/Dr/+eef8fDDD5u3F/R7ca+ZO9SVLA4fPowVK1ZARNCmTRueJuU+JSQkYNq0aVBKIScnB48//jg6\\nduyItLQ0zJo1C0lJSQgICMCwYcPMO6wvW7YMhw8fhqurK/r3749KlSoByP3B/dVXX0EpxdOk5GPO\\nnDk4duwYUlNT4e3tjfDwcDRu3LjI5nzy5EksWLDAfPqInj17GvZcbUF+846Ojsbp06ehlELp0qXR\\nt29f8wr2+vXrsW3bNjg7O992uoL8ftYkJCRg9uzZuHr1KkJCQjB48GA4Ozv0njD3FBMTg/feew8V\\nKlSAUgpKKXTp0gVVqlTh69xC7jTzn376ia91Czl79iwWLFgAXdchImjWrBk6dep0xzllZWVh3rx5\\nOH36NDw9PTFkyBAEBgYCKPz34k4cquARERERkQO9RUtEREREuVjwiIiIiBwMCx4RERGRg2HBIyIi\\nInIwLHhEREREDoYFj4iIiMjBsOARkV1bv349Fi9ebHQMIiKbwvPgEZFNe/nll82XV8rIyECJEiWg\\naRqUUujTpw8ee+wxq2XZtm0bvvnmG5hMJri4uKBSpUp4/fXX4erqig8//BD+/v74z3/+Y7U8RER3\\nUnxPO01EdmHVqlXmjwcNGoR+/fqZL+tjTceOHcPnn3+Od955BxUrVsTVq1fx66+/Wj0HEVFBsOAR\\nkd3I7w2HdevWIT4+HoMHD0ZiYiIGDRqE/v37Y+3atcjMzESXLl1QqVIlLFq0CElJSXj88cfxyiuv\\nmD//xqrc5cuXUaVKFfTt2xcBAQG3fZ0TJ06gevXqqFixIgDA3d0dLVq0AAD88MMP2LVrFzRNw6ZN\\nmxAWFoY33ngDly5dwvLly3H8+HGUKlUKTz/9NJ566ilz7nPnzkHTNBw6dAjlypVD//79zY8fGRmJ\\nzZs349q1a/Dz80OvXr0MKbZEZJ9Y8IjI7t14C/eGuLg4zJs3D8eOHcPUqVNRv359jBkzBllZWRg1\\nahQeffRR1KxZE/v27cOGDRswatQolC1bFpGRkZgzZw7Gjx9/29eoWrUqIiIiEBERgbp166Jy5crm\\na28+8cQTiI2NzfMWrYhg6tSpaNKkCYYOHYqkpCSMHz8e5cuXR506dQAABw4cwOuvv47XXnsN3377\\nLaZNm4a5c+ciPj4eW7ZswZQpU+Dj44OkpCToum7hKRKRI+FBFkTkcF544QU4OzujTp06cHV1RfPm\\nzeHp6Qk/Pz/UqFEDp06dAgD8+OOP6NixI4KCgqBpGjp27IjTp08jKSnptsesUaMGhg8fjtOnT2PK\\nlCno1asXVq1ale+qIpC74peamopOnTpB0zQEBgaibdu22L17t/k+lSpVQpMmTaBpGp599llkZWUh\\nNjYWmqYhOzsb586dQ05ODgICAswXIiciKgiu4BGRw/Hy8jJ/XLJkSXh7e+e5nZGRAQBITEzEypUr\\n8+znBwAmkynft2nr1auHevXqAQCOHj2KmTNnIigoCE888cRt901MTITJZELPnj3N23RdR82aNf+/\\nnft3TRyM4zj+sUSHuNQGpHQuQai0ixQhW8G54OQkdXLJGPC/KO3g0EXo0C4ZpIP0P5BSHMWtXdRC\\noIHswMwAAAHRSURBVNWpUjXYGw4y3F3heuU4Lr5f0xN48iVPhvDh+ZHo2rKsqJ1IJLS1taXpdKpc\\nLqeTkxP5vq/RaKSDgwNVq1VlMpnPvgoAa4qAB2BtWZalcrn8Rydx8/m88vm8hsPhh7Wz2azOz88/\\nrPHy8hK139/fNZlMohDnOI4cx9Hb25suLi50dXUl13U//ZwA1hNLtADWVqlUUrvd1mg0kiTNZjPd\\n3d39sm+v11O329Xr66uk7/v8BoOBbNuWJG1ubioIgqj/7u6uTNPUzc2NFouFVquVhsOhHh4eoj6P\\nj4+6v7/XarVSp9NRMpmUbdt6enpSv99XGIYyDEOpVEobG3yuAfw+ZvAA/Dd+PEzx1RqHh4eaz+c6\\nOzvT8/OzTNPU/v6+isXiT/el02nd3t6q1WppuVwqk8no+PhYjuNIko6OjnR6eqparaa9vT15nqdG\\no6HLy0u5rqswDLWzs6NKpRLVLBQK6na7ajab2t7elud50f676+trjcdjGYYh27ZVr9e/PHYA64Mf\\nHQPAP+D7voIgYNkVwF/BnD8AAEDMEPAAAABihiVaAACAmGEGDwAAIGYIeAAAADFDwAMAAIgZAh4A\\nAEDMEPAAAABihoAHAAAQM98AX7OOAecyhTIAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(,\\n\",\n \" ,\\n\",\n \" )\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"plotting.plot_cost_to_go_mountain_car(env, estimator)\\n\",\n \"plotting.plot_episode_stats(stats, smoothing_window=25)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "FA/Q-Learning with Value Function Approximation.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import itertools\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"import sklearn.pipeline\\n\",\n \"import sklearn.preprocessing\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"\\n\",\n \"from lib import plotting\\n\",\n \"from sklearn.linear_model import SGDRegressor\\n\",\n \"from sklearn.kernel_approximation import RBFSampler\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2016-09-14 09:57:25,042] Making new env: MountainCar-v0\\n\"\n ]\n }\n ],\n \"source\": [\n \"env = gym.envs.make(\\\"MountainCar-v0\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"FeatureUnion(n_jobs=1,\\n\",\n \" transformer_list=[('rbf1', RBFSampler(gamma=5.0, n_components=100, random_state=None)), ('rbf2', RBFSampler(gamma=2.0, n_components=100, random_state=None)), ('rbf3', RBFSampler(gamma=1.0, n_components=100, random_state=None)), ('rbf4', RBFSampler(gamma=0.5, n_components=100, random_state=None))],\\n\",\n \" transformer_weights=None)\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Feature Preprocessing: Normalize to zero mean and unit variance\\n\",\n \"# We use a few samples from the observation space to do this\\n\",\n \"observation_examples = np.array([env.observation_space.sample() for x in range(10000)])\\n\",\n \"scaler = sklearn.preprocessing.StandardScaler()\\n\",\n \"scaler.fit(observation_examples)\\n\",\n \"\\n\",\n \"# Used to convert a state to a featurized representation.\\n\",\n \"# We use RBF kernels with different variances to cover different parts of the space\\n\",\n \"featurizer = sklearn.pipeline.FeatureUnion([\\n\",\n \" (\\\"rbf1\\\", RBFSampler(gamma=5.0, n_components=100)),\\n\",\n \" (\\\"rbf2\\\", RBFSampler(gamma=2.0, n_components=100)),\\n\",\n \" (\\\"rbf3\\\", RBFSampler(gamma=1.0, n_components=100)),\\n\",\n \" (\\\"rbf4\\\", RBFSampler(gamma=0.5, n_components=100))\\n\",\n \" ])\\n\",\n \"featurizer.fit(scaler.transform(observation_examples))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class Estimator():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Value Function approximator. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def __init__(self):\\n\",\n \" # We create a separate model for each action in the environment's\\n\",\n \" # action space. Alternatively we could somehow encode the action\\n\",\n \" # into the features, but this way it's easier to code up.\\n\",\n \" self.models = []\\n\",\n \" for _ in range(env.action_space.n):\\n\",\n \" model = SGDRegressor(learning_rate=\\\"constant\\\")\\n\",\n \" # We need to call partial_fit once to initialize the model\\n\",\n \" # or we get a NotFittedError when trying to make a prediction\\n\",\n \" # This is quite hacky.\\n\",\n \" model.partial_fit([self.featurize_state(env.reset())], [0])\\n\",\n \" self.models.append(model)\\n\",\n \" \\n\",\n \" def featurize_state(self, state):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Returns the featurized representation for a state.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" scaled = scaler.transform([state])\\n\",\n \" featurized = featurizer.transform(scaled)\\n\",\n \" return featurized[0]\\n\",\n \" \\n\",\n \" def predict(self, s, a=None):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Makes value function predictions.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" s: state to make a prediction for\\n\",\n \" a: (Optional) action to make a prediction for\\n\",\n \" \\n\",\n \" Returns\\n\",\n \" If an action a is given this returns a single number as the prediction.\\n\",\n \" If no action is given this returns a vector or predictions for all actions\\n\",\n \" in the environment where pred[i] is the prediction for action i.\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # TODO: Implement this!\\n\",\n \" return 0 if a else np.zeros(env.action_space.n)\\n\",\n \" \\n\",\n \" def update(self, s, a, y):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Updates the estimator parameters for a given state and action towards\\n\",\n \" the target y.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" # TODO: Implement this!\\n\",\n \" return None\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def make_epsilon_greedy_policy(estimator, epsilon, nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates an epsilon-greedy policy based on a given Q-function approximator and epsilon.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" estimator: An estimator that returns q values for a given state\\n\",\n \" epsilon: The probability to select a random action . float between 0 and 1.\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A function that takes the observation as an argument and returns\\n\",\n \" the probabilities for each action in the form of a numpy array of length nA.\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def policy_fn(observation):\\n\",\n \" A = np.ones(nA, dtype=float) * epsilon / nA\\n\",\n \" q_values = estimator.predict(observation)\\n\",\n \" best_action = np.argmax(q_values)\\n\",\n \" A[best_action] += (1.0 - epsilon)\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def q_learning(env, estimator, num_episodes, discount_factor=1.0, epsilon=0.1, epsilon_decay=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Q-Learning algorithm for off-policy TD control using Function Approximation.\\n\",\n \" Finds the optimal greedy policy while following an epsilon-greedy policy.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI environment.\\n\",\n \" estimator: Action-Value function estimator\\n\",\n \" num_episodes: Number of episodes to run for.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" epsilon: Chance the sample a random action. Float betwen 0 and 1.\\n\",\n \" epsilon_decay: Each episode, epsilon is decayed by this factor\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Keeps track of useful statistics\\n\",\n \" stats = plotting.EpisodeStats(\\n\",\n \" episode_lengths=np.zeros(num_episodes),\\n\",\n \" episode_rewards=np.zeros(num_episodes)) \\n\",\n \" \\n\",\n \" for i_episode in range(num_episodes):\\n\",\n \" \\n\",\n \" # The policy we're following\\n\",\n \" policy = make_epsilon_greedy_policy(\\n\",\n \" estimator, epsilon * epsilon_decay**i_episode, env.action_space.n)\\n\",\n \" \\n\",\n \" # Print out which episode we're on, useful for debugging.\\n\",\n \" # Also print reward for last episode\\n\",\n \" last_reward = stats.episode_rewards[i_episode - 1]\\n\",\n \" print(\\\"\\\\rEpisode {}/{} ({})\\\".format(i_episode + 1, num_episodes, last_reward), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \" \\n\",\n \" # TODO: Implement this!\\n\",\n \" \\n\",\n \" return stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"estimator = Estimator()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Episode 100/100 (0.0)\"\n ]\n }\n ],\n \"source\": [\n \"# Note: For the Mountain Car we don't actually need an epsilon > 0.0\\n\",\n \"# because our initial estimate for all states is too \\\"optimistic\\\" which leads\\n\",\n \"# to the exploration of all states.\\n\",\n \"stats = q_learning(env, estimator, 100, epsilon=0.0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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2/HwoUL8eGHH2Lv3r3YuHEj3nrrLQDR76KqqnjttdfQ1NRkqoJi6e7r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\\nzwAAAABJRU5ErkJggg==\\n\",\n 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iZJGjx4sPbu3dtk8CsuLvbYTXrmTvbwP/TOv9E//0b//Be9828ZGRnK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\\nQmCC\\n\",\n 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u7cKSNGjBBHR0eDMF2mXbt2Bp/VdG94qNfGxcTEYPfu3cjKylL+TZ8+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\\nhBBCCCF04l9jFdkPt0mbZAAAAABJRU5ErkJggg==\\n\",\n 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h0AAIDFEegAAAAsjkAHAABgcQQ6AAAAiyPQAQAAWByBDgAAwOIIdAAA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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plotting.plot_cost_to_go_mountain_car(env, estimator)\\n\",\n \"plotting.plot_episode_stats(stats, smoothing_window=25)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "FA/README.md", "content": "## Function Approximation\n\n### Learning Goals\n\n- Understand the motivation for Function Approximation over Table Lookup\n- Understand how to incorporate function approximation into existing algorithms\n- Understand convergence properties of function approximators and RL algorithms\n- Understand batching using experience replay\n\n\n### Summary\n\n- Building a big table, one value for each state or state-action pair, is memory- and data-inefficient. Function Approximation can generalize to unseen states by using a featurized state representation.\n- Treat RL as supervised learning problem with the MC- or TD-target as the label and the current state/action as the input. Often the target also depends on the function estimator but we simply ignore its gradient. That's why these methods are called semi-gradient methods.\n- Challenge: We have non-stationary (policy changes, bootstrapping) and non-iid (correlated in time) data.\n- Many methods assume that our action space is discrete because they rely on calculating the argmax over all actions. Large and continuous action spaces are ongoing research.\n- For Control very few convergence guarantees exist. For non-linear approximators there are basically no guarantees at all. But they tend to work in practice.\n- Experience Replay: Store experience as dataset, randomize it, and repeatedly apply minibatch SGD.\n- Tricks to stabilize non-linear function approximators: Fixed Targets. The target is calculated based on frozen parameter values from a previous time step.\n- For the non-episodic (continuing) case function approximation is more complex and we need to give up discounting and use an \"average reward\" formulation.\n\n\n### Lectures & Readings\n\n**Required:**\n\n- David Silver's RL Course Lecture 6 - Value Function Approximation ([video](https://www.youtube.com/watch?v=UoPei5o4fps), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/FA.pdf))\n- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 9: On-policy Prediction with Approximation\n- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 10: On-policy Control with Approximation\n\n**Optional:**\n\n- [Tutorial: Introduction to Reinforcement Learning with Function Approximation](https://www.youtube.com/watch?v=ggqnxyjaKe4)\n\n\n### Exercises\n\n- Get familiar with the [Mountain Car Playground](MountainCar%20Playground.ipynb)\n\n- Solve Mountain Car Problem using Q-Learning with Linear Function Approximation\n - [Exercise](Q-Learning%20with%20Value%20Function%20Approximation.ipynb)\n - [Solution](Q-Learning%20with%20Value%20Function%20Approximation%20Solution.ipynb)\n" }, { "path": "Introduction/README.md", "content": "## Introduction\n\n### Learning Goals\n\n- Understand the Reinforcement Learning problem and how it differs from Supervised Learning\n\n\n### Summary\n\n- Reinforcement Learning (RL) is concerned with goal-directed learning and decision-making.\n- In RL an agent learns from experiences it gains by interacting with the environment. In Supervised Learning we cannot affect the environment.\n- In RL rewards are often delayed in time and the agent tries to maximize a long-term goal. For example, one may need to make seemingly suboptimal moves to reach a winning position in a game.\n- An agent interacts with the environment via states, actions and rewards.\n\n\n### Lectures & Readings\n\n**Required:**\n\n- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 1: The Reinforcement Learning Problem\n- David Silver's RL Course Lecture 1 - Introduction to Reinforcement Learning ([video](https://www.youtube.com/watch?v=2pWv7GOvuf0), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/intro_RL.pdf))\n- [OpenAI Gym Tutorial](https://gym.openai.com/docs)\n\n**Optional:**\n\nN/A\n\n\n### Exercises\n\n- [Work through the OpenAI Gym Tutorial](https://gym.openai.com/docs)\n" }, { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2016 Denny Britz\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE." }, { "path": "MC/Blackjack Playground.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.blackjack import BlackjackEnv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"env = BlackjackEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Player Score: 19 (Usable Ace: False), Dealer Score: 5\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 27 (Usable Ace: False), Dealer Score: 5\\n\",\n \"Game end. Reward: -1.0\\n\",\n \"\\n\",\n \"Player Score: 21 (Usable Ace: True), Dealer Score: 10\\n\",\n \"Taking action: Stick\\n\",\n \"Player Score: 21 (Usable Ace: True), Dealer Score: 10\\n\",\n \"Game end. Reward: 0.0\\n\",\n \"\\n\",\n \"Player Score: 21 (Usable Ace: True), Dealer Score: 10\\n\",\n \"Taking action: Stick\\n\",\n \"Player Score: 21 (Usable Ace: True), Dealer Score: 10\\n\",\n \"Game end. Reward: 1.0\\n\",\n \"\\n\",\n \"Player Score: 14 (Usable Ace: True), Dealer Score: 10\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 19 (Usable Ace: True), Dealer Score: 10\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 15 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 20 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Taking action: Stick\\n\",\n \"Player Score: 20 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Game end. Reward: 1.0\\n\",\n \"\\n\",\n \"Player Score: 20 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Taking action: Stick\\n\",\n \"Player Score: 20 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Game end. Reward: 1.0\\n\",\n \"\\n\",\n \"Player Score: 18 (Usable Ace: False), Dealer Score: 6\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 27 (Usable Ace: False), Dealer Score: 6\\n\",\n \"Game end. Reward: -1.0\\n\",\n \"\\n\",\n \"Player Score: 16 (Usable Ace: False), Dealer Score: 3\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 18 (Usable Ace: False), Dealer Score: 3\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 23 (Usable Ace: False), Dealer Score: 3\\n\",\n \"Game end. Reward: -1.0\\n\",\n \"\\n\",\n \"Player Score: 19 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 23 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Game end. Reward: -1.0\\n\",\n \"\\n\",\n \"Player Score: 19 (Usable Ace: False), Dealer Score: 4\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 21 (Usable Ace: False), Dealer Score: 4\\n\",\n \"Taking action: Stick\\n\",\n \"Player Score: 21 (Usable Ace: False), Dealer Score: 4\\n\",\n \"Game end. Reward: 1.0\\n\",\n \"\\n\",\n \"Player Score: 21 (Usable Ace: True), Dealer Score: 4\\n\",\n \"Taking action: Stick\\n\",\n \"Player Score: 21 (Usable Ace: True), Dealer Score: 4\\n\",\n \"Game end. Reward: 1.0\\n\",\n \"\\n\",\n \"Player Score: 16 (Usable Ace: True), Dealer Score: 10\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 16 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 26 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Game end. Reward: -1.0\\n\",\n \"\\n\",\n \"Player Score: 14 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 23 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Game end. Reward: -1.0\\n\",\n \"\\n\",\n \"Player Score: 12 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 15 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 16 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 26 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Game end. Reward: -1.0\\n\",\n \"\\n\",\n \"Player Score: 16 (Usable Ace: True), Dealer Score: 8\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 18 (Usable Ace: True), Dealer Score: 8\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 18 (Usable Ace: False), Dealer Score: 8\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 20 (Usable Ace: False), Dealer Score: 8\\n\",\n \"Taking action: Stick\\n\",\n \"Player Score: 20 (Usable Ace: False), Dealer Score: 8\\n\",\n \"Game end. Reward: 1.0\\n\",\n \"\\n\",\n \"Player Score: 20 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Taking action: Stick\\n\",\n \"Player Score: 20 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Game end. Reward: -1.0\\n\",\n \"\\n\",\n \"Player Score: 15 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 16 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 23 (Usable Ace: False), Dealer Score: 10\\n\",\n \"Game end. Reward: -1.0\\n\",\n \"\\n\",\n \"Player Score: 12 (Usable Ace: False), Dealer Score: 4\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 16 (Usable Ace: False), Dealer Score: 4\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 24 (Usable Ace: False), Dealer Score: 4\\n\",\n \"Game end. Reward: -1.0\\n\",\n \"\\n\",\n \"Player Score: 20 (Usable Ace: False), Dealer Score: 7\\n\",\n \"Taking action: Stick\\n\",\n \"Player Score: 20 (Usable Ace: False), Dealer Score: 7\\n\",\n \"Game end. Reward: 1.0\\n\",\n \"\\n\",\n \"Player Score: 15 (Usable Ace: False), Dealer Score: 7\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 21 (Usable Ace: False), Dealer Score: 7\\n\",\n \"Taking action: Stick\\n\",\n \"Player Score: 21 (Usable Ace: False), Dealer Score: 7\\n\",\n \"Game end. Reward: 1.0\\n\",\n \"\\n\",\n \"Player Score: 15 (Usable Ace: False), Dealer Score: 8\\n\",\n \"Taking action: Hit\\n\",\n \"Player Score: 23 (Usable Ace: False), Dealer Score: 8\\n\",\n \"Game end. Reward: -1.0\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"def print_observation(observation):\\n\",\n \" score, dealer_score, usable_ace = observation\\n\",\n \" print(\\\"Player Score: {} (Usable Ace: {}), Dealer Score: {}\\\".format(\\n\",\n \" score, usable_ace, dealer_score))\\n\",\n \"\\n\",\n \"def strategy(observation):\\n\",\n \" score, dealer_score, usable_ace = observation\\n\",\n \" # Stick (action 0) if the score is > 20, hit (action 1) otherwise\\n\",\n \" return 0 if score >= 20 else 1\\n\",\n \"\\n\",\n \"for i_episode in range(20):\\n\",\n \" observation = env.reset()\\n\",\n \" for t in range(100):\\n\",\n \" print_observation(observation)\\n\",\n \" action = strategy(observation)\\n\",\n \" print(\\\"Taking action: {}\\\".format( [\\\"Stick\\\", \\\"Hit\\\"][action]))\\n\",\n \" observation, reward, done, _ = env.step(action)\\n\",\n \" if done:\\n\",\n \" print_observation(observation)\\n\",\n \" print(\\\"Game end. Reward: {}\\\\n\\\".format(float(reward)))\\n\",\n \" break\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "MC/MC Control with Epsilon-Greedy Policies Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"from collections import defaultdict\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.blackjack import BlackjackEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"env = BlackjackEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def make_epsilon_greedy_policy(Q, epsilon, nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates an epsilon-greedy policy based on a given Q-function and epsilon.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" Q: A dictionary that maps from state -> action-values.\\n\",\n \" Each value is a numpy array of length nA (see below)\\n\",\n \" epsilon: The probability to select a random action . float between 0 and 1.\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A function that takes the observation as an argument and returns\\n\",\n \" the probabilities for each action in the form of a numpy array of length nA.\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def policy_fn(observation):\\n\",\n \" A = np.ones(nA, dtype=float) * epsilon / nA\\n\",\n \" best_action = np.argmax(Q[observation])\\n\",\n \" A[best_action] += (1.0 - epsilon)\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def mc_control_epsilon_greedy(env, num_episodes, discount_factor=1.0, epsilon=0.1):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Monte Carlo Control using Epsilon-Greedy policies.\\n\",\n \" Finds an optimal epsilon-greedy policy.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI gym environment.\\n\",\n \" num_episodes: Number of episodes to sample.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" epsilon: Chance the sample a random action. Float betwen 0 and 1.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A tuple (Q, policy).\\n\",\n \" Q is a dictionary mapping state -> action values.\\n\",\n \" policy is a function that takes an observation as an argument and returns\\n\",\n \" action probabilities\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # Keeps track of sum and count of returns for each state\\n\",\n \" # to calculate an average. We could use an array to save all\\n\",\n \" # returns (like in the book) but that's memory inefficient.\\n\",\n \" returns_sum = defaultdict(float)\\n\",\n \" returns_count = defaultdict(float)\\n\",\n \" \\n\",\n \" # The final action-value function.\\n\",\n \" # A nested dictionary that maps state -> (action -> action-value).\\n\",\n \" Q = defaultdict(lambda: np.zeros(env.action_space.n))\\n\",\n \" \\n\",\n \" # The policy we're following\\n\",\n \" policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\\n\",\n \" \\n\",\n \" for i_episode in range(1, num_episodes + 1):\\n\",\n \" # Print out which episode we're on, useful for debugging.\\n\",\n \" if i_episode % 1000 == 0:\\n\",\n \" print(\\\"\\\\rEpisode {}/{}.\\\".format(i_episode, num_episodes), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \"\\n\",\n \" # Generate an episode.\\n\",\n \" # An episode is an array of (state, action, reward) tuples\\n\",\n \" episode = []\\n\",\n \" state = env.reset()\\n\",\n \" for t in range(100):\\n\",\n \" probs = policy(state)\\n\",\n \" action = np.random.choice(np.arange(len(probs)), p=probs)\\n\",\n \" next_state, reward, done, _ = env.step(action)\\n\",\n \" episode.append((state, action, reward))\\n\",\n \" if done:\\n\",\n \" break\\n\",\n \" state = next_state\\n\",\n \"\\n\",\n \" # Find all (state, action) pairs we've visited in this episode\\n\",\n \" # We convert each state to a tuple so that we can use it as a dict key\\n\",\n \" sa_in_episode = set([(tuple(x[0]), x[1]) for x in episode])\\n\",\n \" for state, action in sa_in_episode:\\n\",\n \" sa_pair = (state, action)\\n\",\n \" # Find the first occurance of the (state, action) pair in the episode\\n\",\n \" first_occurence_idx = next(i for i,x in enumerate(episode)\\n\",\n \" if x[0] == state and x[1] == action)\\n\",\n \" # Sum up all rewards since the first occurance\\n\",\n \" G = sum([x[2]*(discount_factor**i) for i,x in enumerate(episode[first_occurence_idx:])])\\n\",\n \" # Calculate average return for this state over all sampled episodes\\n\",\n \" returns_sum[sa_pair] += G\\n\",\n \" returns_count[sa_pair] += 1.0\\n\",\n \" Q[state][action] = returns_sum[sa_pair] / returns_count[sa_pair]\\n\",\n \" \\n\",\n \" # The policy is improved implicitly by changing the Q dictionary\\n\",\n \" \\n\",\n \" return Q, policy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Episode 500000/500000.\"\n ]\n }\n ],\n \"source\": [\n \"Q, policy = mc_control_epsilon_greedy(env, num_episodes=500000, epsilon=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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skncfPNN+OOO+5odGxDhgxxFgYAYNCgQTAYDDh+/Hi7iwO33nor7r77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\\n+zFzAAAAAACAMaawsFCvX7/W27dvFY1Gdf36dS1YsEC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lVVlJeXY8yYMQDgyYyw2WxdjomIiCjasThARORl0KBBEAQBxcXFuOii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\\nYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# For plotting: Create value function from action-value function\\n\",\n \"# by picking the best action at each state\\n\",\n \"V = defaultdict(float)\\n\",\n \"for state, actions in Q.items():\\n\",\n \" action_value = np.max(actions)\\n\",\n \" V[state] = action_value\\n\",\n \"plotting.plot_value_function(V, title=\\\"Optimal Value Function\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "MC/MC Control with Epsilon-Greedy Policies.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"from collections import defaultdict\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.blackjack import BlackjackEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"env = BlackjackEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def make_epsilon_greedy_policy(Q, epsilon, nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates an epsilon-greedy policy based on a given Q-function and epsilon.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" Q: A dictionary that maps from state -> action-values.\\n\",\n \" Each value is a numpy array of length nA (see below)\\n\",\n \" epsilon: The probability to select a random action . float between 0 and 1.\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A function that takes the observation as an argument and returns\\n\",\n \" the probabilities for each action in the form of a numpy array of length nA.\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def policy_fn(observation):\\n\",\n \" pass\\n\",\n \" # Implement this!\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def mc_control_epsilon_greedy(env, num_episodes, discount_factor=1.0, epsilon=0.1):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Monte Carlo Control using Epsilon-Greedy policies.\\n\",\n \" Finds an optimal epsilon-greedy policy.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI gym environment.\\n\",\n \" num_episodes: Number of episodes to sample.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" epsilon: Chance the sample a random action. Float betwen 0 and 1.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A tuple (Q, policy).\\n\",\n \" Q is a dictionary mapping state -> action values.\\n\",\n \" policy is a function that takes an observation as an argument and returns\\n\",\n \" action probabilities\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # Keeps track of sum and count of returns for each state\\n\",\n \" # to calculate an average. We could use an array to save all\\n\",\n \" # returns (like in the book) but that's memory inefficient.\\n\",\n \" returns_sum = defaultdict(float)\\n\",\n \" returns_count = defaultdict(float)\\n\",\n \" \\n\",\n \" # The final action-value function.\\n\",\n \" # A nested dictionary that maps state -> (action -> action-value).\\n\",\n \" Q = defaultdict(lambda: np.zeros(env.action_space.n))\\n\",\n \" \\n\",\n \" # The policy we're following\\n\",\n \" policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\\n\",\n \" \\n\",\n \" # Implement this!\\n\",\n \" \\n\",\n \" return Q, policy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"Q, policy = mc_control_epsilon_greedy(env, num_episodes=500000, epsilon=0.1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# For plotting: Create value function from action-value function\\n\",\n \"# by picking the best action at each state\\n\",\n \"V = defaultdict(float)\\n\",\n \"for state, actions in Q.items():\\n\",\n \" action_value = np.max(actions)\\n\",\n \" V[state] = action_value\\n\",\n \"plotting.plot_value_function(V, title=\\\"Optimal Value Function\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "MC/MC Prediction Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"from collections import defaultdict\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.blackjack import BlackjackEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"env = BlackjackEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def mc_prediction(policy, env, num_episodes, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Monte Carlo prediction algorithm. Calculates the value function\\n\",\n \" for a given policy using sampling.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" policy: A function that maps an observation to action probabilities.\\n\",\n \" env: OpenAI gym environment.\\n\",\n \" num_episodes: Number of episodes to sample.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A dictionary that maps from state -> value.\\n\",\n \" The state is a tuple and the value is a float.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Keeps track of sum and count of returns for each state\\n\",\n \" # to calculate an average. We could use an array to save all\\n\",\n \" # returns (like in the book) but that's memory inefficient.\\n\",\n \" returns_sum = defaultdict(float)\\n\",\n \" returns_count = defaultdict(float)\\n\",\n \" \\n\",\n \" # The final value function\\n\",\n \" V = defaultdict(float)\\n\",\n \" \\n\",\n \" for i_episode in range(1, num_episodes + 1):\\n\",\n \" # Print out which episode we're on, useful for debugging.\\n\",\n \" if i_episode % 1000 == 0:\\n\",\n \" print(\\\"\\\\rEpisode {}/{}.\\\".format(i_episode, num_episodes), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \"\\n\",\n \" # Generate an episode.\\n\",\n \" # An episode is an array of (state, action, reward) tuples\\n\",\n \" episode = []\\n\",\n \" state = env.reset()\\n\",\n \" for t in range(100):\\n\",\n \" action = policy(state)\\n\",\n \" next_state, reward, done, _ = env.step(action)\\n\",\n \" episode.append((state, action, reward))\\n\",\n \" if done:\\n\",\n \" break\\n\",\n \" state = next_state\\n\",\n \"\\n\",\n \" # Find all states the we've visited in this episode\\n\",\n \" # We convert each state to a tuple so that we can use it as a dict key\\n\",\n \" states_in_episode = set([tuple(x[0]) for x in episode])\\n\",\n \" for state in states_in_episode:\\n\",\n \" # Find the first occurance of the state in the episode\\n\",\n \" first_occurence_idx = next(i for i,x in enumerate(episode) if x[0] == state)\\n\",\n \" # Sum up all rewards since the first occurance\\n\",\n \" G = sum([x[2]*(discount_factor**i) for i,x in enumerate(episode[first_occurence_idx:])])\\n\",\n \" # Calculate average return for this state over all sampled episodes\\n\",\n \" returns_sum[state] += G\\n\",\n \" returns_count[state] += 1.0\\n\",\n \" V[state] = returns_sum[state] / returns_count[state]\\n\",\n \"\\n\",\n \" return V \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def sample_policy(observation):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" A policy that sticks if the player score is >= 20 and hits otherwise.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" score, dealer_score, usable_ace = observation\\n\",\n \" return 0 if score >= 20 else 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Episode 10000/10000.\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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4HTCoiI4tSwYcOwevVqqKrqe+zTTz+F1WoNede2V69eyM/Px2effRbw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K1cyd+7cXCl/PB7H7XaTSqVoaWlh//79RCIRmpqaWLFiBYZh0NzcTGdn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\\n\",\n 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bp0wbvvvgugKc9g0qRJ+MlPfoJBgwahpKSkxdV1oKlDIPAK++bNmwEg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\\niIiIYhzDASIiIiIiIqIY9/8B1RI24mJpLSgAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Episode 500000/500000.\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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+WqsUxD13VomoadO3eid+/efp3b814QBKHG0ofa74XS0lKkpqb6808i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\\nFEXhQY9EREREREREWWL3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P+/x+PBnj17Aq6gW61WTJ8+HY8//jjWr1+Pw4cP47PPPgs6vv79+/s7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\\n9/8DlgMqGmq3lRoAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"V_10k = mc_prediction(sample_policy, env, num_episodes=10000)\\n\",\n \"plotting.plot_value_function(V_10k, title=\\\"10,000 Steps\\\")\\n\",\n \"\\n\",\n \"V_500k = mc_prediction(sample_policy, env, num_episodes=500000)\\n\",\n \"plotting.plot_value_function(V_500k, title=\\\"500,000 Steps\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "MC/MC Prediction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"from collections import defaultdict\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.blackjack import BlackjackEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"env = BlackjackEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def mc_prediction(policy, env, num_episodes, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Monte Carlo prediction algorithm. Calculates the value function\\n\",\n \" for a given policy using sampling.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" policy: A function that maps an observation to action probabilities.\\n\",\n \" env: OpenAI gym environment.\\n\",\n \" num_episodes: Number of episodes to sample.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A dictionary that maps from state -> value.\\n\",\n \" The state is a tuple and the value is a float.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Keeps track of sum and count of returns for each state\\n\",\n \" # to calculate an average. We could use an array to save all\\n\",\n \" # returns (like in the book) but that's memory inefficient.\\n\",\n \" returns_sum = defaultdict(float)\\n\",\n \" returns_count = defaultdict(float)\\n\",\n \" \\n\",\n \" # The final value function\\n\",\n \" V = defaultdict(float)\\n\",\n \" \\n\",\n \" # Implement this!\\n\",\n \"\\n\",\n \" return V \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def sample_policy(observation):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" A policy that sticks if the player score is > 20 and hits otherwise.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" score, dealer_score, usable_ace = observation\\n\",\n \" return 0 if score >= 20 else 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true,\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"V_10k = mc_prediction(sample_policy, env, num_episodes=10000)\\n\",\n \"plotting.plot_value_function(V_10k, title=\\\"10,000 Steps\\\")\\n\",\n \"\\n\",\n \"V_500k = mc_prediction(sample_policy, env, num_episodes=500000)\\n\",\n \"plotting.plot_value_function(V_500k, title=\\\"500,000 Steps\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "MC/Off-Policy MC Control with Weighted Importance Sampling Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"from collections import defaultdict\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.blackjack import BlackjackEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"env = BlackjackEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def create_random_policy(nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates a random policy function.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A function that takes an observation as input and returns a vector\\n\",\n \" of action probabilities\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" A = np.ones(nA, dtype=float) / nA\\n\",\n \" def policy_fn(observation):\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def create_greedy_policy(Q):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates a greedy policy based on Q values.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" Q: A dictionary that maps from state -> action values\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A function that takes an observation as input and returns a vector\\n\",\n \" of action probabilities.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def policy_fn(state):\\n\",\n \" A = np.zeros_like(Q[state], dtype=float)\\n\",\n \" best_action = np.argmax(Q[state])\\n\",\n \" A[best_action] = 1.0\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def mc_control_importance_sampling(env, num_episodes, behavior_policy, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Monte Carlo Control Off-Policy Control using Weighted Importance Sampling.\\n\",\n \" Finds an optimal greedy policy.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI gym environment.\\n\",\n \" num_episodes: Number of episodes to sample.\\n\",\n \" behavior_policy: The behavior to follow while generating episodes.\\n\",\n \" A function that given an observation returns a vector of probabilities for each action.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A tuple (Q, policy).\\n\",\n \" Q is a dictionary mapping state -> action values.\\n\",\n \" policy is a function that takes an observation as an argument and returns\\n\",\n \" action probabilities. This is the optimal greedy policy.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # The final action-value function.\\n\",\n \" # A dictionary that maps state -> action values\\n\",\n \" Q = defaultdict(lambda: np.zeros(env.action_space.n))\\n\",\n \" # The cumulative denominator of the weighted importance sampling formula\\n\",\n \" # (across all episodes)\\n\",\n \" C = defaultdict(lambda: np.zeros(env.action_space.n))\\n\",\n \" \\n\",\n \" # Our greedily policy we want to learn\\n\",\n \" target_policy = create_greedy_policy(Q)\\n\",\n \" \\n\",\n \" for i_episode in range(1, num_episodes + 1):\\n\",\n \" # Print out which episode we're on, useful for debugging.\\n\",\n \" if i_episode % 1000 == 0:\\n\",\n \" print(\\\"\\\\rEpisode {}/{}.\\\".format(i_episode, num_episodes), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \"\\n\",\n \" # Generate an episode.\\n\",\n \" # An episode is an array of (state, action, reward) tuples\\n\",\n \" episode = []\\n\",\n \" state = env.reset()\\n\",\n \" for t in range(100):\\n\",\n \" # Sample an action from our policy\\n\",\n \" probs = behavior_policy(state)\\n\",\n \" action = np.random.choice(np.arange(len(probs)), p=probs)\\n\",\n \" next_state, reward, done, _ = env.step(action)\\n\",\n \" episode.append((state, action, reward))\\n\",\n \" if done:\\n\",\n \" break\\n\",\n \" state = next_state\\n\",\n \" \\n\",\n \" # Sum of discounted returns\\n\",\n \" G = 0.0\\n\",\n \" # The importance sampling ratio (the weights of the returns)\\n\",\n \" W = 1.0\\n\",\n \" # For each step in the episode, backwards\\n\",\n \" for t in range(len(episode))[::-1]:\\n\",\n \" state, action, reward = episode[t]\\n\",\n \" # Update the total reward since step t\\n\",\n \" G = discount_factor * G + reward\\n\",\n \" # Update weighted importance sampling formula denominator\\n\",\n \" C[state][action] += W\\n\",\n \" # Update the action-value function using the incremental update formula (5.7)\\n\",\n \" # This also improves our target policy which holds a reference to Q\\n\",\n \" Q[state][action] += (W / C[state][action]) * (G - Q[state][action])\\n\",\n \" # If the action taken by the behavior policy is not the action \\n\",\n \" # taken by the target policy the probability will be 0 and we can break\\n\",\n \" if action != np.argmax(target_policy(state)):\\n\",\n \" break\\n\",\n \" W = W * 1./behavior_policy(state)[action]\\n\",\n \" \\n\",\n \" return Q, target_policy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Episode 500000/500000.\"\n ]\n }\n ],\n \"source\": [\n \"random_policy = create_random_policy(env.action_space.n)\\n\",\n \"Q, policy = mc_control_importance_sampling(env, num_episodes=500000, behavior_policy=random_policy)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n 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s7GrVu3zNq5d0VFu3bt8Omnnxba5+nTpwEADz/8MBRFMV2XJKnIQRKj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\\n/5L2L4QQDkyhUFxT4A+QmJhIYmKihUYkh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kruANy1JUGSJIwbN848C1vQnYMfd/L397+rfkBZ+gaK/rorX2NHKKpI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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# For plotting: Create value function from action-value function\\n\",\n \"# by picking the best action at each state\\n\",\n \"V = defaultdict(float)\\n\",\n \"for state, action_values in Q.items():\\n\",\n \" action_value = np.max(action_values)\\n\",\n \" V[state] = action_value\\n\",\n \"plotting.plot_value_function(V, title=\\\"Optimal Value Function\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "MC/Off-Policy MC Control with Weighted Importance Sampling.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"from collections import defaultdict\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.blackjack import BlackjackEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"env = BlackjackEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def create_random_policy(nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates a random policy function.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A function that takes an observation as input and returns a vector\\n\",\n \" of action probabilities\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" A = np.ones(nA, dtype=float) / nA\\n\",\n \" def policy_fn(observation):\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def create_greedy_policy(Q):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates a greedy policy based on Q values.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" Q: A dictionary that maps from state -> action values\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A function that takes an observation as input and returns a vector\\n\",\n \" of action probabilities.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def policy_fn(observation):\\n\",\n \" pass\\n\",\n \" # Implement this!\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def mc_control_importance_sampling(env, num_episodes, behavior_policy, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Monte Carlo Control Off-Policy Control using Weighted Importance Sampling.\\n\",\n \" Finds an optimal greedy policy.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI gym environment.\\n\",\n \" num_episodes: Number of episodes to sample.\\n\",\n \" behavior_policy: The behavior to follow while generating episodes.\\n\",\n \" A function that given an observation returns a vector of probabilities for each action.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A tuple (Q, policy).\\n\",\n \" Q is a dictionary mapping state -> action values.\\n\",\n \" policy is a function that takes an observation as an argument and returns\\n\",\n \" action probabilities. This is the optimal greedy policy.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # The final action-value function.\\n\",\n \" # A dictionary that maps state -> action values\\n\",\n \" Q = defaultdict(lambda: np.zeros(env.action_space.n))\\n\",\n \" \\n\",\n \" # Our greedily policy we want to learn\\n\",\n \" target_policy = create_greedy_policy(Q)\\n\",\n \" \\n\",\n \" # Implement this!\\n\",\n \" \\n\",\n \" return Q, target_policy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"random_policy = create_random_policy(env.action_space.n)\\n\",\n \"Q, policy = mc_control_importance_sampling(env, num_episodes=500000, behavior_policy=random_policy)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# For plotting: Create value function from action-value function\\n\",\n \"# by picking the best action at each state\\n\",\n \"V = defaultdict(float)\\n\",\n \"for state, action_values in Q.items():\\n\",\n \" action_value = np.max(action_values)\\n\",\n \" V[state] = action_value\\n\",\n \"plotting.plot_value_function(V, title=\\\"Optimal Value Function\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "MC/README.md", "content": "## Model-Free Prediction & Control with Monte Carlo (MC)\n\n\n### Learning Goals\n\n- Understand the difference between Prediction and Control\n- Know how to use the MC method for predicting state values and state-action values\n- Understand the on-policy first-visit MC control algorithm\n- Understand off-policy MC control algorithms\n- Understand Weighted Importance Sampling\n- Understand the benefits of MC algorithms over the Dynamic Programming approach\n\n\n### Summary\n\n- Dynamic Programming approaches assume complete knowledge of the environment (the MDP). In practice, we often don't have full knowledge of how the world works.\n- Monte Carlo (MC) methods can learn directly from experience collected by interacting with the environment. An episode of experience is a series of `(State, Action, Reward, Next State)` tuples.\n- MC methods work based on episodes. We sample episodes of experience and make updates to our estimates at the end of each episode. MC methods have high variance (due to lots of random decisions within an episode) but are unbiased.\n- MC Policy Evaluation: Given a policy, we want to estimate the state-value function V(s). Sample episodes of experience and estimate V(s) to be the reward received from that state onwards averaged across all of your experience. The same technique works for the action-value function Q(s, a). Given enough samples, this is proven to converge.\n- MC Control: Idea is the same as for Dynamic Programming. Use MC Policy Evaluation to evaluate the current policy then improve the policy greedily. The Problem: How do we ensure that we explore all states if we don't know the full environment?\n- Solution to exploration problem: Use epsilon-greedy policies instead of full greedy policies. When making a decision act randomly with probability epsilon. This will learn the optimal epsilon-greedy policy.\n- Off-Policy Learning: How can we learn about the actual optimal (greedy) policy while following an exploratory (epsilon-greedy) policy? We can use importance sampling, which weighs returns by their probability of occurring under the policy we want to learn about.\n\n\n### Lectures & Readings\n\n**Required:**\n\n- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 5: Monte Carlo Methods\n\n\n**Optional:**\n\n- David Silver's RL Course Lecture 4 - Model-Free Prediction ([video](https://www.youtube.com/watch?v=PnHCvfgC_ZA), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MC-TD.pdf))\n- David Silver's RL Course Lecture 5 - Model-Free Control ([video](https://www.youtube.com/watch?v=0g4j2k_Ggc4), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/control.pdf))\n\n\n### Exercises\n\n- Get familiar with the [Blackjack environment (Blackjack-v0)](Blackjack%20Playground.ipynb)\n- Implement the Monte Carlo Prediction to estimate state-action values\n - [Exercise](MC%20Prediction.ipynb)\n - [Solution](MC%20Prediction%20Solution.ipynb)\n- Implement the on-policy first-visit Monte Carlo Control algorithm\n - [Exercise](MC%20Control%20with%20Epsilon-Greedy%20Policies.ipynb)\n - [Solution](MC%20Control%20with%20Epsilon-Greedy%20Policies%20Solution.ipynb)\n- Implement the off-policy every-visit Monte Carlo Control using Weighted Important Sampling algorithm\n - [Exercise](Off-Policy%20MC%20Control%20with%20Weighted%20Importance%20Sampling.ipynb)\n - [Solution](Off-Policy%20MC%20Control%20with%20Weighted%20Importance%20Sampling%20Solution.ipynb)\n" }, { "path": "MDP/README.md", "content": "## MDPs and Bellman Equations\n\n### Learning Goals\n\n- Understand the Agent-Environment interface\n- Understand what MDPs (Markov Decision Processes) are and how to interpret transition diagrams\n- Understand Value Functions, Action-Value Functions, and Policy Functions\n- Understand the Bellman Equations and Bellman Optimality Equations for value functions and action-value functions\n\n\n### Summary\n\n- Agent & Environment Interface: At each step `t` the agent receives a state `S_t`, performs an action `A_t` and receives a reward `R_{t+1}`. The action is chosen according to a policy function `pi`.\n- The total return `G_t` is the sum of all rewards starting from time t . Future rewards are discounted at a discount rate `gamma^k`.\n- Markov property: The environment's response at time `t+1` depends only on the state and action representations at time `t`. The future is independent of the past given the present. Even if an environment doesn't fully satisfy the Markov property we still treat it as if it is and try to construct the state representation to be approximately Markov.\n- Markov Decision Process (MDP): Defined by a state set S, action set A and one-step dynamics `p(s',r | s,a)`. If we have complete knowledge of the environment we know the transition dynamic. In practice, we often don't know the full MDP (but we know that it's some MDP).\n- The Value Function `v(s)` estimates how \"good\" it is for an agent to be in a particular state. More formally, it's the expected return `G_t` given that the agent is in state `s`. `v(s) = Ex[G_t | S_t = s]`. Note that the value function is specific to a given policy `pi`.\n- Action Value function: q(s, a) estimates how \"good\" it is for an agent to be in states and take action a. Similar to the value function, but also considers the action.\n- The Bellman equation expresses the relationship between the value of a state and the values of its successor states. It can be expressed using a \"backup\" diagram. Bellman equations exist for both the value function and the action value function.\n- Value functions define an ordering over policies. A policy `p1` is better than `p2` if `v_p1(s) >= v_p2(s)` for all states s. For MDPs, there exist one or more optimal policies that are better than or equal to all other policies.\n- The optimal state value function `v*(s)` is the value function for the optimal policy. Same for `q*(s, a)`. The Bellman Optimality Equation defines how the optimal value of a state is related to the optimal value of successor states. It has a \"max\" instead of an average.\n\n\n### Lectures & Readings\n\n**Required:**\n\n- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 3: Finite Markov Decision Processes\n- David Silver's RL Course Lecture 2 - Markov Decision Processes ([video](https://www.youtube.com/watch?v=lfHX2hHRMVQ), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf))\n\n\n### Exercises\n\nThis chapter is mostly theory so there are no exercises.\n" }, { "path": "PolicyGradient/CliffWalk Actor Critic Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import itertools\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"import tensorflow as tf\\n\",\n \"import collections\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.cliff_walking import CliffWalkingEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"env = CliffWalkingEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class PolicyEstimator():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Policy Function approximator. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def __init__(self, learning_rate=0.01, scope=\\\"policy_estimator\\\"):\\n\",\n \" with tf.variable_scope(scope):\\n\",\n \" self.state = tf.placeholder(tf.int32, [], \\\"state\\\")\\n\",\n \" self.action = tf.placeholder(dtype=tf.int32, name=\\\"action\\\")\\n\",\n \" self.target = tf.placeholder(dtype=tf.float32, name=\\\"target\\\")\\n\",\n \"\\n\",\n \" # This is just table lookup estimator\\n\",\n \" state_one_hot = tf.one_hot(self.state, int(env.observation_space.n))\\n\",\n \" self.output_layer = tf.contrib.layers.fully_connected(\\n\",\n \" inputs=tf.expand_dims(state_one_hot, 0),\\n\",\n \" num_outputs=env.action_space.n,\\n\",\n \" activation_fn=None,\\n\",\n \" weights_initializer=tf.zeros_initializer)\\n\",\n \"\\n\",\n \" self.action_probs = tf.squeeze(tf.nn.softmax(self.output_layer))\\n\",\n \" self.picked_action_prob = tf.gather(self.action_probs, self.action)\\n\",\n \"\\n\",\n \" # Loss and train op\\n\",\n \" self.loss = -tf.log(self.picked_action_prob) * self.target\\n\",\n \"\\n\",\n \" self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\\n\",\n \" self.train_op = self.optimizer.minimize(\\n\",\n \" self.loss, global_step=tf.contrib.framework.get_global_step())\\n\",\n \" \\n\",\n \" def predict(self, state, sess=None):\\n\",\n \" sess = sess or tf.get_default_session()\\n\",\n \" return sess.run(self.action_probs, { self.state: state })\\n\",\n \"\\n\",\n \" def update(self, state, target, action, sess=None):\\n\",\n \" sess = sess or tf.get_default_session()\\n\",\n \" feed_dict = { self.state: state, self.target: target, self.action: action }\\n\",\n \" _, loss = sess.run([self.train_op, self.loss], feed_dict)\\n\",\n \" return loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class ValueEstimator():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Value Function approximator. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def __init__(self, learning_rate=0.1, scope=\\\"value_estimator\\\"):\\n\",\n \" with tf.variable_scope(scope):\\n\",\n \" self.state = tf.placeholder(tf.int32, [], \\\"state\\\")\\n\",\n \" self.target = tf.placeholder(dtype=tf.float32, name=\\\"target\\\")\\n\",\n \"\\n\",\n \" # This is just table lookup estimator\\n\",\n \" state_one_hot = tf.one_hot(self.state, int(env.observation_space.n))\\n\",\n \" self.output_layer = tf.contrib.layers.fully_connected(\\n\",\n \" inputs=tf.expand_dims(state_one_hot, 0),\\n\",\n \" num_outputs=1,\\n\",\n \" activation_fn=None,\\n\",\n \" weights_initializer=tf.zeros_initializer)\\n\",\n \"\\n\",\n \" self.value_estimate = tf.squeeze(self.output_layer)\\n\",\n \" self.loss = tf.squared_difference(self.value_estimate, self.target)\\n\",\n \"\\n\",\n \" self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\\n\",\n \" self.train_op = self.optimizer.minimize(\\n\",\n \" self.loss, global_step=tf.contrib.framework.get_global_step()) \\n\",\n \" \\n\",\n \" def predict(self, state, sess=None):\\n\",\n \" sess = sess or tf.get_default_session()\\n\",\n \" return sess.run(self.value_estimate, { self.state: state })\\n\",\n \"\\n\",\n \" def update(self, state, target, sess=None):\\n\",\n \" sess = sess or tf.get_default_session()\\n\",\n \" feed_dict = { self.state: state, self.target: target }\\n\",\n \" _, loss = sess.run([self.train_op, self.loss], feed_dict)\\n\",\n \" return loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def actor_critic(env, estimator_policy, estimator_value, num_episodes, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Actor Critic Algorithm. Optimizes the policy \\n\",\n \" function approximator using policy gradient.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI environment.\\n\",\n \" estimator_policy: Policy Function to be optimized \\n\",\n \" estimator_value: Value function approximator, used as a critic\\n\",\n \" num_episodes: Number of episodes to run for\\n\",\n \" discount_factor: Time-discount factor\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Keeps track of useful statistics\\n\",\n \" stats = plotting.EpisodeStats(\\n\",\n \" episode_lengths=np.zeros(num_episodes),\\n\",\n \" episode_rewards=np.zeros(num_episodes)) \\n\",\n \" \\n\",\n \" Transition = collections.namedtuple(\\\"Transition\\\", [\\\"state\\\", \\\"action\\\", \\\"reward\\\", \\\"next_state\\\", \\\"done\\\"])\\n\",\n \" \\n\",\n \" for i_episode in range(num_episodes):\\n\",\n \" # Reset the environment and pick the fisrst action\\n\",\n \" state = env.reset()\\n\",\n \" \\n\",\n \" episode = []\\n\",\n \" \\n\",\n \" # One step in the environment\\n\",\n \" for t in itertools.count():\\n\",\n \" \\n\",\n \" # Take a step\\n\",\n \" action_probs = estimator_policy.predict(state)\\n\",\n \" action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\\n\",\n \" next_state, reward, done, _ = env.step(action)\\n\",\n \" \\n\",\n \" # Keep track of the transition\\n\",\n \" episode.append(Transition(\\n\",\n \" state=state, action=action, reward=reward, next_state=next_state, done=done))\\n\",\n \" \\n\",\n \" # Update statistics\\n\",\n \" stats.episode_rewards[i_episode] += reward\\n\",\n \" stats.episode_lengths[i_episode] = t\\n\",\n \" \\n\",\n \" # Calculate TD Target\\n\",\n \" value_next = estimator_value.predict(next_state)\\n\",\n \" td_target = reward + discount_factor * value_next\\n\",\n \" td_error = td_target - estimator_value.predict(state)\\n\",\n \" \\n\",\n \" # Update the value estimator\\n\",\n \" estimator_value.update(state, td_target)\\n\",\n \" \\n\",\n \" # Update the policy estimator\\n\",\n \" # using the td error as our advantage estimate\\n\",\n \" estimator_policy.update(state, td_error, action)\\n\",\n \" \\n\",\n \" # Print out which step we're on, useful for debugging.\\n\",\n \" print(\\\"\\\\rStep {} @ Episode {}/{} ({})\\\".format(\\n\",\n \" t, i_episode + 1, num_episodes, stats.episode_rewards[i_episode - 1]), end=\\\"\\\")\\n\",\n \"\\n\",\n \" if done:\\n\",\n \" break\\n\",\n \" \\n\",\n \" state = next_state\\n\",\n \" \\n\",\n \" return stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Step 12 @ Episode 300/300 (-13.0)\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"global_step = tf.Variable(0, name=\\\"global_step\\\", trainable=False)\\n\",\n \"policy_estimator = PolicyEstimator()\\n\",\n \"value_estimator = ValueEstimator()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(tf.initialize_all_variables())\\n\",\n \" # Note, due to randomness in the policy the number of episodes you need to learn a good\\n\",\n \" # policy may vary. ~300 seemed to work well for me.\\n\",\n \" stats = actor_critic(env, policy_estimator, value_estimator, 300)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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EhNTSU9PT0ohenxeBpdnb+goCDouNrV3yV81ObhpzYPP7V5+KnNw09t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\\n5fj6+rJgwQI++eQTNm7cyHe+8x1mzZpFTExMq/XLy8uJjIxssw+73U55e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P989913Btu0bt3aYHBxhw4d8ODBg1JdTUUmTJiAoUOHIjIyEtOnT8eJ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\\n\",\n 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URERKQ7KT0LSd0I1esOvUtxGgx5REREpLuyLWs54KKeMeQRERGR7sp+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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(,\\n\",\n \" ,\\n\",\n \" )\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"plotting.plot_episode_stats(stats, smoothing_window=10)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "PolicyGradient/CliffWalk REINFORCE with Baseline Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import itertools\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"import tensorflow as tf\\n\",\n \"import collections\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.cliff_walking import CliffWalkingEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"env = CliffWalkingEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"class PolicyEstimator():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Policy Function approximator. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def __init__(self, learning_rate=0.01, scope=\\\"policy_estimator\\\"):\\n\",\n \" with tf.variable_scope(scope):\\n\",\n \" self.state = tf.placeholder(tf.int32, [], \\\"state\\\")\\n\",\n \" self.action = tf.placeholder(dtype=tf.int32, name=\\\"action\\\")\\n\",\n \" self.target = tf.placeholder(dtype=tf.float32, name=\\\"target\\\")\\n\",\n \"\\n\",\n \" # This is just table lookup estimator\\n\",\n \" state_one_hot = tf.one_hot(self.state, int(env.observation_space.n))\\n\",\n \" self.output_layer = tf.contrib.layers.fully_connected(\\n\",\n \" inputs=tf.expand_dims(state_one_hot, 0),\\n\",\n \" num_outputs=env.action_space.n,\\n\",\n \" activation_fn=None,\\n\",\n \" weights_initializer=tf.zeros_initializer)\\n\",\n \"\\n\",\n \" self.action_probs = tf.squeeze(tf.nn.softmax(self.output_layer))\\n\",\n \" self.picked_action_prob = tf.gather(self.action_probs, self.action)\\n\",\n \"\\n\",\n \" # Loss and train op\\n\",\n \" self.loss = -tf.log(self.picked_action_prob) * self.target\\n\",\n \"\\n\",\n \" self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\\n\",\n \" self.train_op = self.optimizer.minimize(\\n\",\n \" self.loss, global_step=tf.contrib.framework.get_global_step())\\n\",\n \" \\n\",\n \" def predict(self, state, sess=None):\\n\",\n \" sess = sess or tf.get_default_session()\\n\",\n \" return sess.run(self.action_probs, { self.state: state })\\n\",\n \"\\n\",\n \" def update(self, state, target, action, sess=None):\\n\",\n \" sess = sess or tf.get_default_session()\\n\",\n \" feed_dict = { self.state: state, self.target: target, self.action: action }\\n\",\n \" _, loss = sess.run([self.train_op, self.loss], feed_dict)\\n\",\n \" return loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"class ValueEstimator():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Value Function approximator. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def __init__(self, learning_rate=0.1, scope=\\\"value_estimator\\\"):\\n\",\n \" with tf.variable_scope(scope):\\n\",\n \" self.state = tf.placeholder(tf.int32, [], \\\"state\\\")\\n\",\n \" self.target = tf.placeholder(dtype=tf.float32, name=\\\"target\\\")\\n\",\n \"\\n\",\n \" # This is just table lookup estimator\\n\",\n \" state_one_hot = tf.one_hot(self.state, int(env.observation_space.n))\\n\",\n \" self.output_layer = tf.contrib.layers.fully_connected(\\n\",\n \" inputs=tf.expand_dims(state_one_hot, 0),\\n\",\n \" num_outputs=1,\\n\",\n \" activation_fn=None,\\n\",\n \" weights_initializer=tf.zeros_initializer)\\n\",\n \"\\n\",\n \" self.value_estimate = tf.squeeze(self.output_layer)\\n\",\n \" self.loss = tf.squared_difference(self.value_estimate, self.target)\\n\",\n \"\\n\",\n \" self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\\n\",\n \" self.train_op = self.optimizer.minimize(\\n\",\n \" self.loss, global_step=tf.contrib.framework.get_global_step()) \\n\",\n \" \\n\",\n \" def predict(self, state, sess=None):\\n\",\n \" sess = sess or tf.get_default_session()\\n\",\n \" return sess.run(self.value_estimate, { self.state: state })\\n\",\n \"\\n\",\n \" def update(self, state, target, sess=None):\\n\",\n \" sess = sess or tf.get_default_session()\\n\",\n \" feed_dict = { self.state: state, self.target: target }\\n\",\n \" _, loss = sess.run([self.train_op, self.loss], feed_dict)\\n\",\n \" return loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def reinforce(env, estimator_policy, estimator_value, num_episodes, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" REINFORCE (Monte Carlo Policy Gradient) Algorithm. Optimizes the policy\\n\",\n \" function approximator using policy gradient.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI environment.\\n\",\n \" estimator_policy: Policy Function to be optimized \\n\",\n \" estimator_value: Value function approximator, used as a baseline\\n\",\n \" num_episodes: Number of episodes to run for\\n\",\n \" discount_factor: Time-discount factor\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Keeps track of useful statistics\\n\",\n \" stats = plotting.EpisodeStats(\\n\",\n \" episode_lengths=np.zeros(num_episodes),\\n\",\n \" episode_rewards=np.zeros(num_episodes)) \\n\",\n \" \\n\",\n \" Transition = collections.namedtuple(\\\"Transition\\\", [\\\"state\\\", \\\"action\\\", \\\"reward\\\", \\\"next_state\\\", \\\"done\\\"])\\n\",\n \" \\n\",\n \" for i_episode in range(num_episodes):\\n\",\n \" # Reset the environment and pick the first action\\n\",\n \" state = env.reset()\\n\",\n \" \\n\",\n \" episode = []\\n\",\n \" \\n\",\n \" # One step in the environment\\n\",\n \" for t in itertools.count():\\n\",\n \" \\n\",\n \" # Take a step\\n\",\n \" action_probs = estimator_policy.predict(state)\\n\",\n \" action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\\n\",\n \" next_state, reward, done, _ = env.step(action)\\n\",\n \" \\n\",\n \" # Keep track of the transition\\n\",\n \" episode.append(Transition(\\n\",\n \" state=state, action=action, reward=reward, next_state=next_state, done=done))\\n\",\n \" \\n\",\n \" # Update statistics\\n\",\n \" stats.episode_rewards[i_episode] += reward\\n\",\n \" stats.episode_lengths[i_episode] = t\\n\",\n \" \\n\",\n \" # Print out which step we're on, useful for debugging.\\n\",\n \" print(\\\"\\\\rStep {} @ Episode {}/{} ({})\\\".format(\\n\",\n \" t, i_episode + 1, num_episodes, stats.episode_rewards[i_episode - 1]), end=\\\"\\\")\\n\",\n \" # sys.stdout.flush()\\n\",\n \"\\n\",\n \" if done:\\n\",\n \" break\\n\",\n \" \\n\",\n \" state = next_state\\n\",\n \" \\n\",\n \" # Go through the episode and make policy updates\\n\",\n \" for t, transition in enumerate(episode):\\n\",\n \" # The return after this timestep\\n\",\n \" total_return = sum(discount_factor**i * t.reward for i, t in enumerate(episode[t:]))\\n\",\n \" # Calculate baseline/advantage\\n\",\n \" baseline_value = estimator_value.predict(transition.state) \\n\",\n \" advantage = total_return - baseline_value\\n\",\n \" # Update our value estimator\\n\",\n \" estimator_value.update(transition.state, total_return)\\n\",\n \" # Update our policy estimator\\n\",\n \" estimator_policy.update(transition.state, advantage, transition.action)\\n\",\n \" \\n\",\n \" return stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Step 14 @ Episode 2000/2000 (-15.0)\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"global_step = tf.Variable(0, name=\\\"global_step\\\", trainable=False)\\n\",\n \"policy_estimator = PolicyEstimator()\\n\",\n \"value_estimator = ValueEstimator()\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(tf.initialize_all_variables())\\n\",\n \" # Note, due to randomness in the policy the number of episodes you need to learn a good\\n\",\n \" # policy may vary. ~2000-5000 seemed to work well for me.\\n\",\n \" stats = reinforce(env, policy_estimator, value_estimator, 2000, discount_factor=1.0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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NasWdi0aRNqa2uRl5eH0aNH44MPPkBrayucTicAZbDEVVddBUCZ0mTp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\\nT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EOHDjh06BAOHjyo/P3www8W87Rr186iYnLnzp1hMBjK3I4ye+ONNzBs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\\nc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vY3CzOw58Zjz43HnhvPWXsu+fmQLWsgP80FatWD9lQ/qKo1DHnsatWq\\n2W3bhoW/0oDhz1jO+mZhJvbceOy58dhz4zlbz0UE2L0NeuxswNsXWs8XoOo2MrQGe4Y/U872JSIi\\nIiqN5Mh+6Iu+B67kQOv9EtCkZZmb4orhj4iIiJyenDwOPXoWcPoPqIi+UPd1gNIMnxTFEAx/RERE\\n5LQk9Sxk8Q+QxF1Q3XtDDR0D5epqdll2xfBHRERETkcyLkCWzYdsi4Pq+Bi0CV9BVfAwuyxDMPwR\\nERGR05Dsy5BfFkPW/ATV+iFo46ZC+fibXZahGP6IiIiozJO8PMjG1ZBl86AaNIE25hOoIPudUVua\\nMfwRERFRmSW6DonfBImdDVSqCu21sVD31DW7LFMx/BEREVGZJAd2Q180E1AKWr9hUPc2M7ukUoHh\\nj4iIiMoU+f0o9OiZQNo5aE/1A1q1K3Nz9d0Nhj8iIiIqE+TsaUjsbMjRA1CPPwP1YBeocow612NH\\niIiIyKFJugWydC7k181QD/eA1v91KDd3s8sqtRj+iIiIyCFJ1iXIqhjI+hVQbTtB+2AalJeP2WWV\\negx/RERE5FBEBPh1M/RZX0I1ux/avyZDBQaZXZbDYPgjIiIihyEH90CPmQXkXoH2yttQjZubXZLD\\nYfgjIiKiUk9+P2oNfalnoXr0hQp7EErTzC7LITH8ERERUaklySchsXMgxw5CPf4sVLuHeQbvXWL3\\niIiIqNQRS6r1DN7d26C6PgVtwN+g3NzMLqtMYPgjIiKiUkMyL0JWLIJs/gWqfVdo4/8L5elldlll\\nCsMfERERmU5ysiG/LIH8shiqVTto70VB+QWaXVaZxPBHREREppG8XMjG1ZBl86EaNIE2+hOoytXM\\nLqtMY/gjIiIiw4muQ3ZsgCyeA1SpDu31sVA165pdllNg+CMiIiLDiAiwL946bUt5N+ul2BqGml2W\\nU2H4IyIiIkPI0QPQo2cClzKg9ewHNLsfSimzy3I6DH9ERERkV3LyOPSY2cDJ36F6PAfVJhxKczG7\\nLKfF8EdERER2ISnJkCU/QA4kQHXrBTVkNJSrq9llOT2GPyIiIipRcvE8ZOl8yI4NUJ0ehzZhCJS7\\nh9ll0V8Y/oiIiKhESNYlyOoYSNwKqAc6QvvgP1DevmaXRddh+CMiIqK7IrlXIOuWQVZGQ4WGQfvX\\nZKjAILPLoptg+CMiIqI7Ivn5kC1rID/NBe6pC23UBKjqNc0ui26D4Y+IiIiKRUSA3VutZ/D6+EF7\\n5e9QdRuZXRYVEcMfERERFZkc2mudqy83F9ozA4GQlpyrz8Ew/BEREdFtyYkka+hLSYaKeB4q7EEo\\nTTO7LLoDDH9ERER0U5J8Epemz4N+cB/U489APdgFqhzjgyPjs0dEREQ3EEsqZOlcyO5tKP/EM8h/\\nfhiUm7vZZVEJYPgjIiIiG8m8CFmxCLL5F6j2XaGN/y/cq1RFbkaG2aVRCWH4IyIiIkhONuSXJZBf\\nFkO1agftvSgov0CzyyI7YPgjIiJyYpKXC9m4GrJsPlSDJtBGfwJVuZrZZZEdMfwRERE5IdF1yI4N\\nkMVzgCrVob0+FqpmXbPLIgMw/BERETkREQH2xUOPmQW4uUPr/wZUwyZml0UGYvgjIiJyEnL0APTo\\n74GsS9Ce6gc0u48TNDshhj8iIqIyTk4ehx49Czj9B9STz0G16QCluZhdFpmE4Y+IiKiMkpRkyOI5\\nkIN7oLr3hho6BsrV1eyyyGQMf0RERGWMXDgPWTYPsnMjVOcnoD0/FMrdw+yyqJRg+CMiIiojJOsS\\nZFUMZP0KqAc6QRv3HyhvX7PLolKG4Y+IiMjBSe4VyLplkJXRUKFh0P41BSqwktllUSnF8EdEROSg\\nJD8fsnUt5KcfgZp1oY2aAFW9ptllUSnH8EdERORgRARI2G6dq8/bB9rgv0PVbWR2WeQgGP6IiIgc\\niBzeb52r70oOtN4DgCatOFcfFQvDHxERkQOQP36DHjMTSD4F1aMv1H3toTTN7LLIATH8ERERlWKS\\nkgyJnQM5vNc6V9+wf0CV41x9dOcY/oiIiEohuXgesnQeZMdfc/X1exXKvYLZZVEZwPBHRERUisjl\\nLMiqaEjcCqgHOkL7gHP1Ucli+CMiIioFJPcKJG4FZMVCqCatoP1rMlRgkNllURnE8EdERGQi0fMh\\nW+MgS34AatSGNmo8VPV7zC6LyjCGPyIiIhOICLBnO/ToWYCnN7SXR0HVa2x2WeQEGP6IiIgMJkf2\\nQ4+eCWRfhtarPxAaxrn6yDAMf0RERAaRk8etI32n/7DO1Xd/eyjNxeyyyMkw/BEREdmZpCRDFs+B\\nHNxjnatv6BgoV87VR+Zg+CMiIrITuXgesmwBZPt6qE6PQ3t+KJS7h9llkZNj+CMiIiphcjkLsjoW\\nsm4ZVJtwaOO+hPLxM7ssIgAMf0RERCVGcnMh65dDli+EatIS2j8/g6pY2eyyiAowLPxNmzYNu3bt\\ngq+vLyZNmgQAWLBgAdasWQNfX+vM5X369EHz5s0BADExMVi3bh1cXFzQv39/NGvWDACQkJCAGTNm\\nQETQsWNHREREGLULREREhRI9H7ItDrLkR6D6PdBGfgAVXMvssogKZVj469ixI7p164apU6cWWP74\\n44/j8ccfL7Ds5MmT2Lp1KyZPnoy0tDR88MEHiIqKgohg+vTpGDt2LPz9/TFmzBi0bt0a1atXN2o3\\niIiIbKxz9e2AHjML8PCENnAkVH3O1Uelm2Hhr1GjRkhJSblhuYjcsCw+Ph5t27aFi4sLgoKCULVq\\nVSQlJUFEULVqVVSqVAkA0K5dO+zcuZPhj4iIDCdHD0CP/h64nAWt5wtA09acq48cgunH/K1atQob\\nNmxA3bp18cILL8DDwwMWiwUNGjSwrRMQEACLxQIRQWBgYIHlSUlJZpRNREROSk7+bh3pO3UC6snn\\noNp04Fx95FBMDX+PPPIIevXqBaUU5s6di5kzZ2LIkCGFjgYqpW66nIiIyN4kJRmy5EdI4i7rXH1D\\nRnOuPnJIpoY/Hx8f28+dO3fGxx9/DAAIDAxEamqq7XdpaWnw9/eHiBRYbrFY4O/vX+i2ExMTkZiY\\naLsdGRkJb2/vkt4FuoXy5cuz5wZjz43HnhvP6J7rF84jO2Y2cjf9gvKPPAX3waOgPDwNe/zSgK9z\\nc8yfP9/2c0hICEJCQkpku4aGPxEpMHqXnp4OPz/rvEfbt29HjRo1AABhYWGIiorC448/DovFguTk\\nZNSrVw8iguTkZKSkpMDf3x+bN2/GG2+8UehjFdakjIwMO+0ZFcbb25s9Nxh7bjz23HhG9Vyy/5qr\\nb+0yqPs7QL0/FXk+fsjM1wEne875Ojeet7c3IiMj7bJtw8Lf559/jgMHDiAjIwNDhw5FZGQkEhMT\\n8fvvv0MphUqVKmHw4MEAgODgYDzwwAMYMWIEypUrh0GDBkEpBaUUBg4ciPHjx0NE0KlTJwQHBxu1\\nC0RE5ASsc/WtgKxYCNW4ObR/fApVqYrZZRGVGCWFHUhXRp0+fdrsEpwKPykajz03HntuPHv1XPR8\\nyPYNkMVzgGo1ofXsBxVcu8QfxxHxdW68atWq2W3bpp/tS0REZCYRAfbGQ4+ZCbhXgPbSCKgGJXNs\\nFVFpxPBHREROS5IOQF80E8jKhPZUP6DZfZxFgso8hj8iInI6cuqEda6+P49D9XgOqk045+ojp8Hw\\nR0RETkPSzkEWz4Hs3wXVrRfUK3+Hci1vdllEhmL4IyKiMk8yLkCWzYdsi4Pq2B3ahK+gKniYXRaR\\nKRj+iIiozLLO1bcYsnYp1H3toY2bCuVT+MUBiJwFwx8REZU5kpsL2bAKsnw+VKNmnKuP6BoMf0RE\\nVGaIng/ZFgdZ8qN1rr433oOqWcfssohKFYY/IiJyeCIC7NkOPXoW4OkFbeBIqPqNzS6LqFRi+CMi\\nIocmR/ZDj54JZF+G1qs/EBrGufqIboHhj4iIHJL88Zt1rr4zf0JF9IW6rz3n6iMqAoY/IiJyKPnJ\\np6D/8DXk8D6o7r2hhr0DVc7V7LKIHAbDHxEROQS5cB6ydB4yf90EdHoCWr9hUO4VzC6LyOEw/BER\\nUakmWZcgq6Ih61dCte0E709n4pLSzC6LyGEx/BERUakkV3Ig65ZDVkVDNW0N7V9ToAIrQfP2BjIy\\nzC6PyGEx/BERUaki+fmQLWsgP80FatWD9tZEqKo1zC6LqMxg+CMiolJBRIBdW6DHzgZ8A6ANeRuq\\nTkOzyyIqcxj+iIjIdHJwD/RF3wOiQ3t2MNC4OefqI7IThj8iIjKN/H7UOkFz2jmoiOehWrWD0ngy\\nB5E9MfwREZHhJPkkJHYO5NhBqMefhWr3MFQ5/pdEZAT+SyMiIsPI+TTITz9Cdm+D6hoBbcDfoNzc\\nzC6LyKkw/BERkd3JpQzIikWQTT9DPdQV2vhpUJ7eZpdF5JQY/oiIyG4kJxuy5ifIz4uhWj4A7d0o\\nKP9As8sicmoMf0REVOIkLw+y6WfIsnlQ9RpDe/tjqCrVzS6LiMDwR0REJUh0HRK/CRI7G6hUBdqw\\nf0DVqm92WUR0DYY/IiK6ayICJO6GHjMT0Fyg9RsGdW8zs8siokIw/BER0V2RpIPW0HcxHVpEP6Dl\\nA5ygmagUY/gjIqI7IiePQ4+ZDZz8HerJPlBtOkK5uJhdFhHdBsMfEREVi5w7DVn8A+TwPqhuvaCG\\njIZydTW7LCIqIoY/IiIqEjmfBlk6F7JrC9TDPazH9blXMLssIiomhj8iIrolybgIWbkQsnnNXxM0\\n/5cTNBM5MIY/IiIqlGRnQX5eAln7E1TYg9Dei4Ly4wTNRI6O4Y+IiAqQ3CuQuBWQlYugGjeH9s6n\\nUJWqmF0WEZUQhj8iIgLwV+jbuBqyMhq4py60EeOggmuZXRYRlTCGPyIiJyd5eZAtayBL5wE160Ab\\nOgaqNq/KQVRWMfwRETkp26XYFv8ABFSENuRtqDoNzS6LiOyM4Y+IyMmICLA3HnrsLMC1PLTnh/JS\\nbEROhOGPiMiJyOH91kuxXc6CFvE80Px+XoqNyMkw/BEROQE5kQQ9ZhZw9jTUk89B3d8eSuOl2Iic\\nEcMfEVEZJmf+hB47B/jtENRjkVAPdoEqx0uxETkzhj8iojJIzpy0Xort4B6oLhFQL42AcnMzuywi\\nKgUY/oiIyhBJSYb8NBeyLx6qSw9o/V6FcvcwuywiKkUY/oiIygCxpEKWzYfs2gzV8TFoE76C8vA0\\nuywiKoUY/oiIHJhcPA9ZvhCyLQ7qoa7QPpgG5eVjdllEVIox/BEROSC5nAVZHQtZtwyqTTi096dC\\n+fqbXRYROYBihb+MjAzs3r0b58+fR48ePWCxWCAiCAwMtFd9RER0Dcm9Alm/ArJ8IVSTltD++RlU\\nxcpml0VEDkQr6ooHDhzA3/72N2zcuBGLFi0CACQnJ+Obb76xW3FERGQlej70zWug/3Mo5OBeaCM/\\ngPbSCAY/Iiq2Io/8zZgxA3/7298QGhqKAQMGAADq1auHY8eO2a04IiJnJyLA7m3QY2cDXt7QXh4F\\nVa+x2WURkQMrcvhLSUlBaGhowTuXK4f8/PwSL4qIiAA5tBd69EwgNxda75eAJi15KTYiumtFDn/B\\nwcFISEhA8+bNbcv27duHmjVr2qUwIiJnJSeSoEfPAlLOQPXoC9X6ISityEfpEBHdUpHDX79+/fDx\\nxx+jRYsWuHLlCr7++mv8+uuveOutt+xZHxGR05DkU5DFcyBHD0A9zkuxEZF9KBGRoq5ssViwceNG\\npKSkoGLFinjooYcc6kzf06dPm12CU/H29kZGRobZZTgV9tx4JdFzsaRaL8W2e6v1Umydn4Bycy+h\\nCssevs6Nx54br1q1anbbdrGmegkICECPHj3sVQsRkVORSxmQFQshm36xTtA8/r9Qnt5ml0VEZdwt\\nw98XX3xRpIOLhw8fXmIFERGVdZKTDfllCeSXxVAt20F7NwrK33G+RSEix3bLI4irVKmCypUro3Ll\\nyvDw8MDOnTuh6zoCAgKg6zp27twJDw9eMJyIqCgkLxf6umXQ//EKcOoEtNGfQOv3KoMfERnqliN/\\nvXv3tv08YcIEjB49Gvfee69t2aFDh2wTPhMRUeFE1yE7NkAWzwGqVIf2+liomnXNLouInFSRj/k7\\ncuQI6tevX2BZvXr1cOTIkRIvioioLBARYF889JhZQHk3aP1fh2oYevs7EhHZUZHDX+3atfHjjz/i\\nmWeeQfmG4HnaAAAgAElEQVTy5XHlyhXMnz8ftWrVsmN5RESOSY4esE7QfCkDWs9+QLP7OUEzEZUK\\nRQ5/r776KqKiovDiiy/Cy8sLmZmZqFu3Ll5//XV71kdE5FDkRBL0xT8Ap/+AerIPVJtwKM3F7LKI\\niGyKHP6CgoIwfvx4pKam4vz58/D390fFihXtWRsRkcOQP49DX/ID8PtRqEd7QQ0dA+XKCZqJqPQp\\n1jx/mZmZSExMhMViQUBAAFq1agUvLy971UZEVOrlnzkJ/YevIYf3QT36NNTLb0KVdzO7LCKimyrW\\nCR8ffvghqlevjooVK2LXrl2YMWMGxowZgwYNGtz2/tOmTcOuXbvg6+uLSZMmAbCGySlTpiAlJQVB\\nQUEYMWKEbeqYb7/9FgkJCXBzc8OwYcNsxxbGxcUhJiYGANCzZ0906NChuPtMRHTXrl6VIzNhG9Dp\\nCWgvDIdyr2B2WUREt1Xk8DdjxgwMGjQI7dq1sy3bsmULvvvuO3z44Ye3vX/Hjh3RrVs3TJ061bYs\\nNjYWoaGh6NGjB2JjYxETE4O+ffti9+7dOHv2LKKionD06FF88803mDBhAjIzM7Fo0SJ8/PHHEBGM\\nHj0arVu35lyDRGQYybgIWbEAsnkN1ENd4T15Fi4JT+QgIsdxy0mer3XmzBk88MADBZa1adMGycnJ\\nRbp/o0aN4OnpWWBZfHy8beQuPDwc8fHxAICdO3faltevXx9ZWVlIT0/Hnj170LRpU3h4eMDT0xNN\\nmzZFQkJCUXeBiOiOSXYW9CU/Qv/XUCD3CrT3v4DWqz80Lx+zSyMiKpYij/xVqVIFW7ZswYMPPmhb\\ntnXrVlSuXPmOH/zChQvw8/MDAPj5+eHChQsAAIvFgsDA/5/xPiAgABaL5abLiYjsRXKvQNYth6xc\\nBBXSAto7k6CCqppdFhHRHSty+Ovfvz8++ugjrFixAhUrVkRKSgrOnDmD0aNH27M+G6WUdcLUIkpM\\nTERiYqLtdmRkJLy9ecF0I5UvX549Nxh7XnIkPx9X4lYge9FMuNRpgAr/+gwuNevcsB57bjz23Hjs\\nuTnmz59v+zkkJAQhISElst0ih7+GDRviiy++wK5du3D+/Hm0atUKLVu2vKuzff38/JCenm7729fX\\nF4B1RC8tLc22XlpaGvz9/REYGFgg0KWlpaFJkyaFbruwJmVkZNxxrVR83t7e7LnB2PO7J7oO2bkR\\nsuRHwD8Q2uC3IHUbIQsACukte2489tx47LnxvL29ERkZaZdtF2uqFy8vL7Rv3x4AcPbsWVy+fLlY\\n4U9ECozetWrVCnFxcYiIiEBcXBzCwsIAAGFhYVi1ahXatm2LI0eOwNPTE35+fmjWrBnmzp2LrKws\\n6LqOffv2oW/fvsXZBSKiQokIsHubda4+N3dofYcA9zbjVTmIqMxRUsTvUqdMmYJu3bqhYcOGWLdu\\nHf73v/9B0zQMGDAAnTp1uu39P//8cxw4cAAZGRnw9fVFZGQkWrdujcmTJyM1NRUVK1bEyJEjbSeF\\nTJ8+HQkJCXB3d8fQoUNRp47165a4uDhER0dDKVXsqV5Onz5d5HXp7vGTovHY8+ITESBxF/TYOYCe\\nDy3ieSA0rMihjz03HntuPPbceNWqVbPbtosc/gYNGoT//ve/KFeuHEaNGoWXX34Znp6e+OSTTxAV\\nFWW3AksSw5+x+GZhPPa8eOTwfuixs63X3+3xHNDiASityJMgAGDPzcCeG489N549w1+Rv/bNy8tD\\nuXLlYLFYkJmZiUaNGgGA7QxdIiJHIUkHoC/5EUg9C/VEH6j72/P6u0TkNIoc/mrVqoWYmBikpKSg\\nZcuWAKxTslSowBnticgxyG+HoS/+ATh7CuqxSKgHOkGVK9ahz0REDq/I73pDhgzBvHnz4OLign79\\n+gGwXvLt2nn/iIhKIzl+1Hoix+kTUN0jodp1hirnanZZRESmKPIxf2UBj/kzFo8RMR57XpCcOwN9\\n/nTgxDGo7r2hHuwC5VqyoY89Nx57bjz23HimHfO3YcMG29Qua9euvel6RTnbl4jIKJJ5EbIqBrJ+\\nJVTnx6Fe+TuUa3mzyyIiKhVuGf42b95sC38bN2686XoMf0RUGkh2FuTnJZC1P0G1bAvtvSiogEpm\\nl0VEVKrwa1+yG35NYDxn7blcyYHELYesjIYKaWE9g9eg6+86a8/NxJ4bjz03XqmY6gUALl26ZLu8\\nm7+/P1q2bGmblJmIyGiSlwfZ/Atk6Tygdn1oo8ZDVb/H7LKIiEq1Ioe//fv3Y9KkSahWrRoqVqyI\\ntLQ0TJ8+HaNGjUJoaKg9ayQiKkD0fMiODdbr71aqAu3Vd6Bq1ze7LCIih1Dk8Dd9+nQMHjwYbdu2\\ntS3bunUrpk+fjilTptilOCKia4muA7u2WCdo9vCE9uJrUA354ZOIqDiKHP7Onz+PNm3aFFh23333\\n4auvvirxooiIriUiwO6t1tDnWh5a75eAJi2LfP1dIiL6f0UOf+3bt8fKlSvRvXt327LVq1fbzgYm\\nIippIgLs3WmdoBmA9tQLQNMwhj4iortQ5PB3/Phx/Pzzz1iyZAkCAgJgsVhw4cIF1K9fH++++65t\\nvffff98uhRKR8xARIHG3NfRdyYH25HNAizYMfUREJaDI4a9z587o3LmzPWshIicnIsCeHdCXzQdy\\nsqGeeBaqVTsoTTO7NCKiMuO24e/bb7/FSy+9hPDwcADWK31cO6nzpEmT8Oabb9qtQCIq+2wncixb\\nAChAe+wZ60gfQx8RUYm77Tvr+vXrC9yeNWtWgdv79u0r2YqIyGlIfj70bXHQ33sN+upYaBHPQ/vX\\nFKhWbRn8iIjs5LYjf7e7AIgTXSCEiEqI6Dpk50bIT3MBb19ozw4C7m3OY/qIiAxw2/B3uzdjvlkT\\nUVFZp2zZZj2Rw80d2nOvAPc24/sIEZGBbhv+8vPzsX//ftttXddvuE1EdDtycA/0hTMACLSeLwCh\\nnLKFiMgMtw1/vr6+mDZtmu22l5dXgds+Pj72qYyIygQ5edw6OfPxo9CefRlo+QBDHxGRiW4b/r78\\n8ksj6iCiMkb+OAZ96Tzgt8NQXSOgBo2CKu9mdllERE6vyPP8EREVhRw7ZJ2n78/foB7pCTVwFJQb\\nQx8RUWnB8EdEJUKSDkD/aR6QfBLq0aehho6Gci1vdllERHQdhj8iumOi68C+eOgrFwHpFqhuT0O1\\n7QxVztXs0oiI6CYY/oio2CQvF7J9A2RVNODqah3pa9kWysXF7NKIiOg2GP6IqMgkLw+yZQ1k2Xyg\\ncjXr2bucp4+IyKEw/BHRbUleHmTbOsjSedbQN/gtqLqNzC6LiIjuAMMfEd2U5OVCtq6DLF8AVKwM\\nbeBIqPqNzS6LiIjuAsMfEd1Acq9AtqyFrFhoHekbOAKqHkMfEVFZwPBHRDaScRGybhlk/QrgnnrQ\\nXn6TX+8SEZUxDH9EBElLgfyyGLJlLVSrttDenABVtYbZZRERkR0w/BE5MbGkQpbNh/y6GapdZ2jv\\nfQHlH2h2WUREZEcMf0ROSC6ch6xYCNkWB/VQV2jjp0F5+ZhdFhERGYDhj8iJSHoaZGU0ZOs6qAc6\\nQnt/KpSvv9llERGRgRj+iJyAWFIgKxdBtm+AatvJGvr8Aswui4iITMDwR1SGSdo569e7OzdBPdgF\\n2gdfQvlwpI+IyJkx/BGVQZJ6FrJ8AWTXVqj2j1iP6fP2NbssIiIqBRj+iMoQPS0F+vxvrWfvtu/G\\nEzmIiOgGDH9EZcDVs3cztq8HHuwC7QOGPiIiKhzDH5EDk4vnIasXQzb9DNUmHN6TvsMlF1ezyyIi\\nolKM4Y/IAUnaOciqGMj29VD3t4c2dgpUQCVo3t5ARobZ5RERUSnG8EfkQCT5JGTFIsieHdazd8d9\\nyXn6iIioWBj+iByA/HEMsnwh5Mh+qE6PQZvwFZSnl9llERGRA2L4IyrFJOkA9OULgT9/g+oSAa3/\\n61DuFcwui4iIHBjDH1EpIyLAgQToy+cDllSoR5+GGjoGypUnchAR0d1j+CMqJUTPBxK2W0f6ruRA\\nde8N1fohKBcXs0sjIqIyhOGPyGRyKQOy6WfIuuWArz+0xyKBZvdBaZrZpRERURnE8EdkEjl5HLJ2\\nmfVqHE3vg/bK21C165tdFhERlXEMf0QGkvx8IGEb9LVLgXNnoDp0s16Nw8fP7NKIiMhJMPwRGUAy\\nLkI2roKsXwEEBEF1ehyqRRuocvwnSERExuL/PER2JCeOQdYuhSRsg2rRBtqwf0DVrGt2WURE5MQY\\n/ohKmOTmQn7dBIlbAZxPhQrvDm38V1DePmaXRkRExPBHVFIkOwuybT1k5SIgMAjaIz2Bpq05VQsR\\nEZUqDH9Ed0mST0HW/ATZsQFo2ATaC8OhGjc3uywiIqJCMfwR3SE5/Qdk6TzIob1QDz0C7b0voPwD\\nzS6LiIjolhj+iIpBRICjByDrlkEO74PqGmEd6eP1domIyEEw/BEVgZxPg2xZA9myBnApB9W+K7QX\\nX2PoIyIih8PwR3QToucDe3dC3/gzkHQQKqwdtIEjgdoNoJQyuzwiIqI7wvBHdB3JzoJsWAVZuwzw\\n9Ydq/yjU4Leg3NzNLo2IiOiuMfwR/UWysyCbfoasjAZq1IH2yt+hajcwuywiIqISVSrC37Bhw+Dh\\n4QGlFFxcXPDhhx8iMzMTU6ZMQUpKCoKCgjBixAh4eHgAAL799lskJCTAzc0Nw4YNQ61atczdAXJo\\nkp4GWbMUsmk1VKNm0IaOgarbyOyyiIiI7KJUhD+lFN599114eXnZlsXGxiI0NBQ9evRAbGwsYmJi\\n0LdvX+zevRtnz55FVFQUjh49im+++QYTJkwwsXpyVHLqBGR1LCRhO1SbcGjvfApVqYrZZREREdmV\\nZnYBgHX6DBEpsCw+Ph4dOnQAAISHhyM+Ph4AsHPnTtvy+vXrIysrC+np6cYWTA5LRCAH9yD/8/eh\\nTx4LBFWFNvEraH0GM/gREZFTKDUjfxMmTIBSCg8//DA6d+6MCxcuwM/PDwDg5+eHCxcuAAAsFgsC\\nA/9/It2AgABYLBbbukSFkUuZkB3rIRtXA7m5UF0joF4dA+Va3uzSiIiIDFUqwt/48ePh5+eHixcv\\nYvz48ahWrVqx7s9pN+hm5PgRyNplkD07oEJaQHu6P3BvMyitVAx6ExERGa5UhL+ro3Y+Pj5o3bo1\\nkpKS4Ofnh/T0dNvfvr6+AKwjfWlpabb7pqWlwd/f/4ZtJiYmIjEx0XY7MjIS3t7edt4Tulb58uVN\\n6bno+cjbvR3Z0bOAi+lw6xqB8gPfgObta3gtRjOr586MPTcee2489twc8+fPt/0cEhKCkJCQEtmu\\n6eEvJycHIgJ3d3dkZ2dj79696NWrF1q1aoW4uDhEREQgLi4OYWFhAICwsDCsWrUKbdu2xZEjR+Dp\\n6VnoV76FNSkjI8OQfSIrb29vQ3suF89DNq+FbFwFuFeACu8G9WAX5GouyAUAJ3j+je45sedmYM+N\\nx54bz9vbG5GRkXbZtunh78KFC/jkk0+glEJ+fj4eeughNGvWDHXr1sXkyZOxbt06VKxYESNHjgQA\\ntGzZErt378Zrr70Gd3d3DB061OQ9IDPJpUzIri2QnRuB35OgWj5gvQpHnYY8HICIiKgQSq4/zbYM\\nO336tNklOBV7fVIUEeDwPujrlgMHE4DGzaG1bg80aQXl5lbij+dI+OnceOy58dhz47Hnxivu+Q/F\\nYfrIH1FRSXYWZGscZN0yAIDq+BjUi69BeXiaXBkREZHjYPijUk9O/g7ZsBKyfQPQqCm0vkOABk34\\ntS4REdEdYPijUkmu5EDiN0E2rALSUqAefBjau1FQARXNLo2IiMihMfxRqSEX0yH74oF9v0IO7gHq\\nNIT2aE8gtDWUi4vZ5REREZUJDH9kKsm4ANm9DfLrFuD4EaiQFkBoGLRnX4byCzC7PCIiojKH4Y8M\\nJxfPQ3Ztg/y6GTiRBBXSEurBLtbLrbm5m10eERFRmcbwR4YQEcjWdZDNvwB/HocKbQWt42NASEun\\nn56FiIjISAx/ZFdiSYWsWQLZvQ3QdWiRA4HQVlCu5c0ujYiIyCkx/FGJk+wsyK5tyPx1E/TDiVBh\\nbaENGQ3UqM3pWYiIiEzG8EclQnQdOLQH+vpVwME9QIMQlA/vBn3w2/xal4iIqBRh+KM7JrlXgIN7\\nIAnbIXt3AhU8oNo9DNXvVSgvH5T39kYOLwdERERUqjD8UbHJhfOQhTMgCdusX+U2vx/aIz2hKtvv\\nOoRERERUMhj+6LZEBEhJhuyLt07PcuoEVGgYtA+mcS4+IiIiB8PwR4WSrEvAgd2Qfb9CDu0F9Hyo\\ne5tDe+RpoHFzKFdXs0skIiKiO8DwRwD+Gt0786d1dG/fr8DvSUD9e6GatobW7WmgcnWeqUtERFQG\\nMPw5McnJAQ7thez/K/CJQDUNg9YlAmgUyqttEBERlUEMf05GsjKtgW/PTsiWNUDDUOvxe6+PBarW\\n4OgeERFRGcfw5wQk+RRk2zrIgQTgzJ9AvXutx+99MA2qSnWzyyMiIiIDMfyVQZKWAjm8Dzi8z/r3\\nlRzrdCxP9QPqNebJGkRERE6M4a8MEEsK5PD+/w97OdlQDZoAjUKhPdoTqBLMr3OJiIgIAMOfQ5Lz\\naZDDe4HD+61h73IW0LAJVMNQ68ka1XjsHhERERWO4c8BSLrFGvKOJEJO/Q4cOwS0fACqQSi0h5+0\\nnqihaWaXSURERA6A4a+UEhHI9vWQnxcDySeBBiFQIS2gtXoAuKcelKe32SUSERGRA2L4K0UkJwdI\\n3AXZ/yskcReQr0M9+hRUp8ehNBezyyMiIqIygOGvFJDcK5AfvoLs2QH4BUA90Ml67F4VXlWDiIiI\\nShbDn0nkSo712rm7tkF2bAACK0F7dQxQpxGP3yMiIiK7Yfgzgeg69KnjgfNpUB0egda9F1SVYLPL\\nIiIiIifA8GcQSUsB/jwGsaRCFn0PePtCe/tjKP9As0sjIiIiJ8LwZ2dy7jRkx0bIymigclWoWg2g\\n+gyG9mAXs0sjIiIiJ8TwZ0eSkgz93deAe5tBPdUPqmN3Hs9HREREpmL4swPJyYbEzobs3gbVsi20\\nl0eZXRIRERERAIa/EicpydA/eQcIqgqt9wCgSSuzSyIiIiKyYfgrQZKeBn3CKKj7HoLq8wrn6CMi\\nIqJShweglSB9zldAw1AGPyIiIiq1GP5KiL7wO+DgHmiPPMXgR0RERKUWv/YtAXIkEbIqBtqk76F8\\n/c0uh4iIiOimOPJ3l0QEesxMqG69GPyIiIio1GP4u0uyPQ747TDUY5Fml0JERER0Wwx/d0EO74NM\\nnwz10ggoN3ezyyEiIiK6LYa/OySZF6H/71OoF4ZDu7+D2eUQERERFQlP+LgDcuIY9HnfALUaQLV7\\n2OxyiIiIiIqMI3/FJLm50MePgLqnHrQBb/BavURERORQOPJXDPLrZkjCDqBKMLRnBpldDhEREVGx\\nMfwVgb7pZ8imn4Fjh6C6PgXtlbfMLomIiIjojjD83YaIQKJnQvV+Cepv70O5VzC7JCIiIqI7xvB3\\nO2dPAeXdoD3Q0exKiIiIiO4az1a4BRGBrIwGqtU0uxQiIiKiEsHwdwuyZglk8y/QevQ1uxQiIiKi\\nEsGvfQshIpD4TZCV0VAvDIe6p67ZJRERERGVCIa/QsimnyHL5kM9/CRUq7Zml0NERERUYhj+CiGb\\nf4F6tCe08O5ml0JERERUonjM3zVEBHJgN/DHb1ANmphdDhEREVGJ48jftXZvhT7rP1DdewNVa5hd\\nDREREVGJY/i7hsRvhurSA1r33maXQkRERGQX/Nr3L5J9GbJnB1SdhmaXQkRERGQ3DH9XnTsNeHkD\\nDUPNroSIiIjIbhj+rrKkAMG1oZQyuxIiIiIiu2H4AyAZF6DP+S8ncyYiIqIyj+EPsE7oXK8x1BN9\\nzC6FiIiIyK6cPvxZ5/ZLgHrkKX7lS0RERGWe0071IpZUyLGDwKkTQE42UKOO2SURERER2Z3Dhr+E\\nhATMmDEDIoKOHTsiIiKiWPeXJT9Akk9CVb8H2vNDoVxc7FQpERERUenhkOFP13VMnz4dY8eOhb+/\\nP8aMGYPWrVujevXqt72vnD0N2RYH2bUV2ph/Q/FKHkREROREHPKYv6SkJFStWhWVKlVCuXLl0K5d\\nO+zcufO299O3rIH+3nDg3Blob01k8CMiIiKn45AjfxaLBYGBgbbbAQEBSEpKuu39ZOk8aENGQzW7\\nz57lEREREZVaDjnyV5iinKmr7m0OhIYZUA0RERFR6eSQI38BAQFITU213bZYLPD39y+wTmJiIhIT\\nE223IyMjEfz2eMNqJCtvb2+zS3A67Lnx2HPjsefGY8+NN3/+fNvPISEhCAkJKZHtOuTIX7169ZCc\\nnIyUlBTk5eVh8+bNCAsrOKIXEhKCyMhI259rG0jGYM+Nx54bjz03HntuPPbcePPnzy+QY0oq+AEO\\nOvKnaRoGDhyI8ePHQ0TQqVMnBAcHm10WERERUannkOEPAJo3b47PP//c7DKIiIiIHIpDfu17J0py\\nuJSKhj03HntuPPbceOy58dhz49mz50pExG5bJyIiIqJSxWlG/oiIiIiI4Y+IiIjIqTjsCR/FkZCQ\\ngBkzZkBE0LFjR0RERJhdkkMbNmwYPDw8oJSCi4sLPvzwQ2RmZmLKlClISUlBUFAQRowYAQ8PDwDA\\nt99+i4SEBLi5uWHYsGGoVasWACAuLg4xMTEAgJ49e6JDhw5m7VKpM23aNOzatQu+vr6YNGkSAJRo\\nj3/77Tf85z//QW5uLlq0aIH+/fsbvo+lTWE9X7BgAdasWQNfX18AQJ8+fdC8eXMAQExMDNatWwcX\\nFxf0798fzZo1A3Dz95tz587h888/R2ZmJmrXro3XXnsNLi4uJuxp6ZGWloapU6ciPT0dmqahc+fO\\n6N69O1/rdnJ9vx9++GF069aNr3M7y83Nxbvvvou8vDzk5+ejTZs26N279017lZeXh6lTp+K3336D\\nt7c3RowYgYoVKwIo/vNxU1LG5efny/Dhw+XcuXOSm5srb775ppw8edLsshzasGHDJCMjo8CyWbNm\\nSWxsrIiIxMTEyOzZs0VEZNeuXTJx4kQRETly5Ii88847IiKSkZEhw4cPl0uXLklmZqbtZ7I6ePCg\\nHD9+XEaNGmVbVpI9HjNmjBw9elRERCZOnCi7d+82bN9Kq8J6Pn/+fPnpp59uWPfPP/+Ut956S/Ly\\n8uTs2bMyfPhw0XX9lu83n332mWzZskVERL7++mtZvXq1MTtWip0/f16OHz8uIiKXL1+W119/XU6e\\nPMnXup3crN98ndtfdna2iFgzyTvvvCNHjhy5aa9WrVol33zzjYiIbN68WSZPniwid/Z83EyZ/9o3\\nKSkJVatWRaVKlVCuXDm0a9cOO3fuNLsshyYikOvOE4qPj7d90g4PD0d8fDwAYOfOnbbl9evXR1ZW\\nFtLT07Fnzx40bdoUHh4e8PT0RNOmTZGQkGDsjpRijRo1gqenZ4FlJdXj9PR0XL58GfXq1QMAtG/f\\nnv8mUHjPAdzwWgesz0Xbtm3h4uKCoKAgVK1aFUlJSbd8v9m/fz/uv/9+AECHDh2wY8cO++6QA/Dz\\n87ON3Lm7u6N69epIS0vja91OCuu3xWIBwNe5vbm5uQGwjgLm5+dDKYXExMQCvbraw2tf523atMH+\\n/fsB3NnzcTNl/mtfi8WCwMBA2+2AgAAkJSWZWJHjU0phwoQJUErh4YcfRufOnXHhwgX4+fkBsL7B\\nXLhwAUDh/bdYLDddTjdXUj2+fnlgYCB7fwurVq3Chg0bULduXbzwwgvw8PCAxWJBgwYNbOtc7a2I\\nFPp+k5GRAS8vL2ia9fN2YGAgzp8/b/i+lGbnzp3DiRMn0KBBA77WDXC13/Xr18ehQ4f4OrczXdcx\\nevRonD17Fo888ggqV64MT0/PAr26+tq89nWraRo8PDyQmZlZ7OfjVsp8+CuMUsrsEhza+PHj4efn\\nh4sXL2L8+PGoVq1ase6vlCr0UyaVnFv1uLDl/DdRuEceeQS9evWCUgpz587FzJkzMWTIkJv28FbL\\nr/8de/7/srOz8dlnn6F///5wd3cv1n35Wi++6/vN17n9aZqGf//738jKysKkSZNw6tSpG9a5Xa+K\\n+3zcsp7b1OvwAgICkJqaarttsVjg7+9vYkWO7+onch8fH7Ru3RpJSUnw8/NDeno6ACA9Pd124HBA\\nQADS0tJs901LS4O/vz8CAwMLPC9paWkICAgwcC8cT0n1ODAwsND16UY+Pj62N9HOnTvbPk0X1lt/\\nf/+bvt/4+Pjg0qVL0HW9wPoE5Ofn49NPP0X79u3RunVrAHyt21Nh/ebr3DgeHh5o3Lgxjhw5ctNe\\nXfs613UdWVlZ8PLyKvbzcStlPvzVq1cPycnJSElJQV5eHjZv3oywsDCzy3JYOTk5yM7OBmD99Lh3\\n717UrFkTrVq1QlxcHADrWXdXexwWFob169cDAI4cOQJPT0/4+fmhWbNm2LdvH7KyspCZmYl9+/bZ\\nzloiq+s/RZdUj/38/FChQgUkJSVBRLBhwwbbfwLO7vqeXw0gALB9+3bUqFEDgLXnW7ZsQV5eHs6d\\nO4fk5GTUq1ev0Pebq71t0qQJtm3bBgBYv379/7V3dyFRdXscx78zTD2WmK+JWVSYmaKYQUlldVF2\\nkQSJROWNZJEljL1K3kRddKMIpom9XGQlVKSQStgLlDeVhRgJ1RSSJo2F5jTRi6aOzjwXnfY5PSqn\\nTsdenN/nas9mzdprr73Y/Flr7bX0HvqX48ePM2PGDFJSUoxzautjZ6T6VjsfW+/fv6e3txeAgYEB\\nHj58yIwZM4iNjR2xrv6znd+9e5e4uDjj/Lc+j/9W716xw0dzczOnT5/G4/GwcuVKLfXyA16/fk1h\\nYSEmk4mhoSGWL19OamoqHz9+5MiRIzgcDkJCQti7d68xef7UqVM0Nzfj4+NDdnY2ERERwOeX+qVL\\nl9N9MZsAAAWuSURBVDCZTFrq5R9KSkqw2Wx8+PABf39/NmzYwKJFi/5vddzW1kZZWZmx/EVmZuYv\\nu9ffxUh1/vjxY9rb2zGZTEydOpWsrCyj57u6upr6+nosFsuwJRdGet+8fv2a4uJienp6mD17Njk5\\nOVgsXjnzxvD06VMOHTrEzJkzMZlMmEwm0tPTiYyMVFsfA6PV9+3bt9XOx9CLFy8oKyvD7Xbj8XhY\\nunQpaWlpo9aVy+WitLSU9vZ2/Pz82LVrF6GhocD3P4/ReEXwJyIiIiKfjfthXxERERH5NwV/IiIi\\nIl5EwZ+IiIiIF1HwJyIiIuJFFPyJiIiIeBEFfyIiIiJeRMGfiIxb1dXVnDx58lcXQ0Tkt6J1/kTk\\nj5WRkWFsS9XX18eECRMwm82YTCa2bdvGsmXLflpZ6uvruXz5Mk6nk7/++ouIiAh2796Nj48Px44d\\nIzg4mI0bN/608oiIjMa7l90WkT9aRUWFcWy1WtmxY4exFdLPZLPZuHDhAgcOHGDWrFn09PRw//79\\nn14OEZFvoeBPRMaFkQYxqqqq6OzsJCcnh+7ubqxWK9nZ2Vy8eJH+/n7S09OJiIjgxIkTOBwOli9f\\nzpYtW4z/f+nNe/fuHZGRkWRlZRESEjLsOq2trcybN49Zs2YB4Ovry4oVKwC4ceMGt27dwmw2c+XK\\nFWJjY9m/fz9v376lvLycJ0+eMGnSJFJSUlizZo1Rbrvdjtls5sGDB0ybNo3s7Gwj/5qaGq5du8an\\nT58ICgpi69atvyToFZE/k4I/ERnXvgwLf/Hs2TNKS0ux2WwUFBSwYMECDh48iMvlIi8vjyVLlhAT\\nE0NjYyO1tbXk5eURFhZGTU0NJSUlHD58eNg15s6dS2VlJZWVlcyfP585c+YY+5kmJyfT0tLy1bCv\\nx+OhoKCAxMRE9uzZg8Ph4PDhw0yfPp34+HgAmpqa2L17Nzt37qSuro7CwkKOHj1KZ2cn169fJz8/\\nn4CAABwOB263e4xrUUTGE33wISJeZf369VgsFuLj4/Hx8SEpKQk/Pz+CgoKIjo7m+fPnANy8eZPU\\n1FTCw8Mxm82kpqbS3t6Ow+EYlmd0dDT79u2jvb2d/Px8tm7dSkVFxYi9kfC5p/DDhw+kpaVhNpsJ\\nDQ1l1apV3Llzx0gTERFBYmIiZrOZtWvX4nK5aGlpwWw2Mzg4iN1uZ2hoiJCQEGPTdxGRb6GePxHx\\nKlOmTDGOJ06ciL+//1e/+/r6AOju7ubMmTNfzSsEcDqdIw79JiQkkJCQAMCjR48oKioiPDyc5OTk\\nYWm7u7txOp1kZmYa59xuNzExMcbv4OBg49hkMhEUFMTbt2+Jjo5m8+bNVFVV0dHRwfz588nIyCAw\\nMPB7q0JEvJSCPxGREQQHB5OWlvY/fTEcFxdHXFwcdrt91LxDQ0MpKSkZNY83b94Yxx6PB6fTaQR4\\nSUlJJCUl0dfXx8mTJzl37hxWq/W7yyki3knDviIiI1i9ejXV1dV0dHQA0Nvby71790ZM29TUREND\\nAz09PcDneYU2m42oqCgAAgIC6OrqMtJHRkYyefJkamtrGRgYwO12Y7fbaW1tNdK0tbXR2NiI2+2m\\nrq6OCRMmEBUVxatXr3j06BGDg4NYLBYmTpyI2axXuYh8O/X8ici48M8PO340j8TERPr7+ykuLsbh\\ncDB58mTi4+NZvHjxsP/5+vpy9epVysvLcblcBAYGsm7dOpKSkgBYuXIlRUVFZGZmEhsbS25uLnl5\\neZw9exar1crg4CDh4eFs2rTJyHPhwoU0NDRQVlZGWFgYubm5xny/8+fP8/LlSywWC1FRUWzfvv2H\\n711EvIcWeRYR+c1UVVXR1dWloVwRGRMaKxARERHxIgr+RERERLyIhn1FREREvIh6/kRERES8iII/\\nERERES+i4E9ERETEiyj4ExEREfEiCv5EREREvIiCPxEREREv8jdDaZhvxWR++gAAAABJRU5ErkJg\\ngg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"(,\\n\",\n \" ,\\n\",\n \" )\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"plotting.plot_episode_stats(stats, smoothing_window=25)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n" }, { "path": "PolicyGradient/Continuous MountainCar Actor Critic Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import itertools\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"import tensorflow as tf\\n\",\n \"import collections\\n\",\n \"\\n\",\n \"import sklearn.pipeline\\n\",\n \"import sklearn.preprocessing\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"from lib.envs.cliff_walking import CliffWalkingEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"from sklearn.kernel_approximation import RBFSampler\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2017-06-16 13:11:05,265] Making new env: MountainCarContinuous-v0\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"array([-0.21213569, 0.03012651])\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"env = gym.envs.make(\\\"MountainCarContinuous-v0\\\")\\n\",\n \"env.observation_space.sample()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"FeatureUnion(n_jobs=1,\\n\",\n \" transformer_list=[('rbf1', RBFSampler(gamma=5.0, n_components=100, random_state=None)), ('rbf2', RBFSampler(gamma=2.0, n_components=100, random_state=None)), ('rbf3', RBFSampler(gamma=1.0, n_components=100, random_state=None)), ('rbf4', RBFSampler(gamma=0.5, n_components=100, random_state=None))],\\n\",\n \" transformer_weights=None)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Feature Preprocessing: Normalize to zero mean and unit variance\\n\",\n \"# We use a few samples from the observation space to do this\\n\",\n \"observation_examples = np.array([env.observation_space.sample() for x in range(10000)])\\n\",\n \"scaler = sklearn.preprocessing.StandardScaler()\\n\",\n \"scaler.fit(observation_examples)\\n\",\n \"\\n\",\n \"# Used to converte a state to a featurizes represenation.\\n\",\n \"# We use RBF kernels with different variances to cover different parts of the space\\n\",\n \"featurizer = sklearn.pipeline.FeatureUnion([\\n\",\n \" (\\\"rbf1\\\", RBFSampler(gamma=5.0, n_components=100)),\\n\",\n \" (\\\"rbf2\\\", RBFSampler(gamma=2.0, n_components=100)),\\n\",\n \" (\\\"rbf3\\\", RBFSampler(gamma=1.0, n_components=100)),\\n\",\n \" (\\\"rbf4\\\", RBFSampler(gamma=0.5, n_components=100))\\n\",\n \" ])\\n\",\n \"featurizer.fit(scaler.transform(observation_examples))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def featurize_state(state):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Returns the featurized representation for a state.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" scaled = scaler.transform([state])\\n\",\n \" featurized = featurizer.transform(scaled)\\n\",\n \" return featurized[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class PolicyEstimator():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Policy Function approximator. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def __init__(self, learning_rate=0.01, scope=\\\"policy_estimator\\\"):\\n\",\n \" with tf.variable_scope(scope):\\n\",\n \" self.state = tf.placeholder(tf.float32, [400], \\\"state\\\")\\n\",\n \" self.target = tf.placeholder(dtype=tf.float32, name=\\\"target\\\")\\n\",\n \"\\n\",\n \" # This is just linear classifier\\n\",\n \" self.mu = tf.contrib.layers.fully_connected(\\n\",\n \" inputs=tf.expand_dims(self.state, 0),\\n\",\n \" num_outputs=1,\\n\",\n \" activation_fn=None,\\n\",\n \" weights_initializer=tf.zeros_initializer)\\n\",\n \" self.mu = tf.squeeze(self.mu)\\n\",\n \" \\n\",\n \" self.sigma = tf.contrib.layers.fully_connected(\\n\",\n \" inputs=tf.expand_dims(self.state, 0),\\n\",\n \" num_outputs=1,\\n\",\n \" activation_fn=None,\\n\",\n \" weights_initializer=tf.zeros_initializer)\\n\",\n \" \\n\",\n \" self.sigma = tf.squeeze(self.sigma)\\n\",\n \" self.sigma = tf.nn.softplus(self.sigma) + 1e-5\\n\",\n \" self.normal_dist = tf.contrib.distributions.Normal(self.mu, self.sigma)\\n\",\n \" self.action = self.normal_dist._sample_n(1)\\n\",\n \" self.action = tf.clip_by_value(self.action, env.action_space.low[0], env.action_space.high[0])\\n\",\n \"\\n\",\n \" # Loss and train op\\n\",\n \" self.loss = -self.normal_dist.log_prob(self.action) * self.target\\n\",\n \" # Add cross entropy cost to encourage exploration\\n\",\n \" self.loss -= 1e-1 * self.normal_dist.entropy()\\n\",\n \" \\n\",\n \" self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\\n\",\n \" self.train_op = self.optimizer.minimize(\\n\",\n \" self.loss, global_step=tf.contrib.framework.get_global_step())\\n\",\n \" \\n\",\n \" def predict(self, state, sess=None):\\n\",\n \" sess = sess or tf.get_default_session()\\n\",\n \" state = featurize_state(state)\\n\",\n \" return sess.run(self.action, { self.state: state })\\n\",\n \"\\n\",\n \" def update(self, state, target, action, sess=None):\\n\",\n \" sess = sess or tf.get_default_session()\\n\",\n \" state = featurize_state(state)\\n\",\n \" feed_dict = { self.state: state, self.target: target, self.action: action }\\n\",\n \" _, loss = sess.run([self.train_op, self.loss], feed_dict)\\n\",\n \" return loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class ValueEstimator():\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Value Function approximator. \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def __init__(self, learning_rate=0.1, scope=\\\"value_estimator\\\"):\\n\",\n \" with tf.variable_scope(scope):\\n\",\n \" self.state = tf.placeholder(tf.float32, [400], \\\"state\\\")\\n\",\n \" self.target = tf.placeholder(dtype=tf.float32, name=\\\"target\\\")\\n\",\n \"\\n\",\n \" # This is just linear classifier\\n\",\n \" self.output_layer = tf.contrib.layers.fully_connected(\\n\",\n \" inputs=tf.expand_dims(self.state, 0),\\n\",\n \" num_outputs=1,\\n\",\n \" activation_fn=None,\\n\",\n \" weights_initializer=tf.zeros_initializer)\\n\",\n \"\\n\",\n \" self.value_estimate = tf.squeeze(self.output_layer)\\n\",\n \" self.loss = tf.squared_difference(self.value_estimate, self.target)\\n\",\n \"\\n\",\n \" self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\\n\",\n \" self.train_op = self.optimizer.minimize(\\n\",\n \" self.loss, global_step=tf.contrib.framework.get_global_step()) \\n\",\n \" \\n\",\n \" def predict(self, state, sess=None):\\n\",\n \" sess = sess or tf.get_default_session()\\n\",\n \" state = featurize_state(state)\\n\",\n \" return sess.run(self.value_estimate, { self.state: state })\\n\",\n \"\\n\",\n \" def update(self, state, target, sess=None):\\n\",\n \" sess = sess or tf.get_default_session()\\n\",\n \" state = featurize_state(state)\\n\",\n \" feed_dict = { self.state: state, self.target: target }\\n\",\n \" _, loss = sess.run([self.train_op, self.loss], feed_dict)\\n\",\n \" return loss\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def actor_critic(env, estimator_policy, estimator_value, num_episodes, discount_factor=1.0):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Actor Critic Algorithm. Optimizes the policy \\n\",\n \" function approximator using policy gradient.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI environment.\\n\",\n \" estimator_policy: Policy Function to be optimized \\n\",\n \" estimator_value: Value function approximator, used as a critic\\n\",\n \" num_episodes: Number of episodes to run for\\n\",\n \" discount_factor: Time-discount factor\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \"\\n\",\n \" # Keeps track of useful statistics\\n\",\n \" stats = plotting.EpisodeStats(\\n\",\n \" episode_lengths=np.zeros(num_episodes),\\n\",\n \" episode_rewards=np.zeros(num_episodes)) \\n\",\n \" \\n\",\n \" Transition = collections.namedtuple(\\\"Transition\\\", [\\\"state\\\", \\\"action\\\", \\\"reward\\\", \\\"next_state\\\", \\\"done\\\"])\\n\",\n \" \\n\",\n \" for i_episode in range(num_episodes):\\n\",\n \" # Reset the environment and pick the fisrst action\\n\",\n \" state = env.reset()\\n\",\n \" \\n\",\n \" episode = []\\n\",\n \" \\n\",\n \" # One step in the environment\\n\",\n \" for t in itertools.count():\\n\",\n \" \\n\",\n \" # env.render()\\n\",\n \" \\n\",\n \" # Take a step\\n\",\n \" action = estimator_policy.predict(state)\\n\",\n \" next_state, reward, done, _ = env.step(action)\\n\",\n \" \\n\",\n \" # Keep track of the transition\\n\",\n \" episode.append(Transition(\\n\",\n \" state=state, action=action, reward=reward, next_state=next_state, done=done))\\n\",\n \" \\n\",\n \" # Update statistics\\n\",\n \" stats.episode_rewards[i_episode] += reward\\n\",\n \" stats.episode_lengths[i_episode] = t\\n\",\n \" \\n\",\n \" # Calculate TD Target\\n\",\n \" value_next = estimator_value.predict(next_state)\\n\",\n \" td_target = reward + discount_factor * value_next\\n\",\n \" td_error = td_target - estimator_value.predict(state)\\n\",\n \" \\n\",\n \" # Update the value estimator\\n\",\n \" estimator_value.update(state, td_target)\\n\",\n \" \\n\",\n \" # Update the policy estimator\\n\",\n \" # using the td error as our advantage estimate\\n\",\n \" estimator_policy.update(state, td_error, action)\\n\",\n \" \\n\",\n \" # Print out which step we're on, useful for debugging.\\n\",\n \" print(\\\"\\\\rStep {} @ Episode {}/{} ({})\\\".format(\\n\",\n \" t, i_episode + 1, num_episodes, stats.episode_rewards[i_episode - 1]), end=\\\"\\\")\\n\",\n \"\\n\",\n \" if done:\\n\",\n \" break\\n\",\n \" \\n\",\n \" state = next_state\\n\",\n \" \\n\",\n \" return stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING:tensorflow:From /Users/dennybritz/venv/py3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py:170: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\\n\",\n \"Instructions for updating:\\n\",\n \"Use `tf.global_variables_initializer` instead.\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2017-06-16 13:31:05,772] From /Users/dennybritz/venv/py3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py:170: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\\n\",\n \"Instructions for updating:\\n\",\n \"Use `tf.global_variables_initializer` instead.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Step 662 @ Episode 50/50 (65.13252566564918))\"\n ]\n }\n ],\n \"source\": [\n \"tf.reset_default_graph()\\n\",\n \"\\n\",\n \"global_step = tf.Variable(0, name=\\\"global_step\\\", trainable=False)\\n\",\n \"policy_estimator = PolicyEstimator(learning_rate=0.001)\\n\",\n \"value_estimator = ValueEstimator(learning_rate=0.1)\\n\",\n \"\\n\",\n \"with tf.Session() as sess:\\n\",\n \" sess.run(tf.initialize_all_variables())\\n\",\n \" # Note, due to randomness in the policy the number of episodes you need varies\\n\",\n \" # TODO: Sometimes the algorithm gets stuck, I'm not sure what exactly is happening there.\\n\",\n \" stats = actor_critic(env, policy_estimator, value_estimator, 50, discount_factor=0.95)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"plotting.plot_episode_stats(stats, smoothing_window=10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "PolicyGradient/README.md", "content": "## Policy Gradient Methods\n\n\n### Learning Goals\n\n- Understand the difference between value-based and policy-based Reinforcement Learning\n- Understand the REINFORCE Algorithm (Monte Carlo Policy Gradient)\n- Understand Actor-Critic (AC) algorithms\n- Understand Advantage Functions\n- Understand Deterministic Policy Gradients (Optional)\n- Understand how to scale up Policy Gradient methods using asynchronous actor-critic and Neural Networks (Optional)\n\n\n### Summary\n\n- Idea: Instead of parameterizing the value function and doing greedy policy improvement we parameterize the policy and do gradient descent into a direction that improves it.\n- Sometimes the policy is easier to approximate than the value function. Also, we need a parameterized policy to deal with continuous action spaces and environments where we need to act stochastically.\n- Policy Score Function `J(theta)`: Intuitively, it measures how good our policy is. For example, we can use the average value or average reward under a policy as our objective.\n- Common choices for the policy function: Softmax for discrete actions, Gaussian parameters for continuous actions.\n- Policy Gradient Theorem: `grad(J(theta)) = Ex[grad(log(pi(s, a))) * Q(s, a)]`. Basically, we move our policy into a direction of more reward.\n- REINFORCE (Monte Carlo Policy Gradient): We substitute a samples return `g_t` form an episode for Q(s, a) to make an update. Unbiased but high variance.\n- Baseline: Instead of measuring the absolute goodness of an action we want to know how much better than \"average\" it is to take an action given a state. E.g. some states are naturally bad and always give negative reward. This is called the advantage and is defined as `Q(s, a) - V(s)`. We use that for our policy update, e.g. `g_t - V(s)` for REINFORCE.\n- Actor-Critic: Instead of waiting until the end of an episode as in REINFORCE we use bootstrapping and make an update at each step. To do that we also train a Critic Q(theta) that approximates the value function. Now we have two function approximators: One of the policy, one for the critic. This is basically TD, but for Policy Gradients.\n- A good estimate of the advantage function in the Actor-Critic algorithm is the td error. Our update then becomes `grad(J(theta)) = Ex[grad(log(pi(s, a))) * td_error]`.\n- Can use policy gradients with td-lambda, eligibility traces, and so on.\n- Deterministic Policy Gradients: Useful for high-dimensional continuous action spaces where stochastic policy gradients are expensive to compute. The idea is to update the policy in the direction of the gradient of the action-value function. To ensure exploration we can use an off-policy actor-critic algorithm with added noise in action selection.\n- Deep Deterministic Policy Gradients: Apply tricks from DQN to Deterministic Policy Gradients ;)\n- Asynchronous Advantage Actor-Critic (A3C): Instead of using an experience replay buffer as in DQN use multiple agents on different threads to explore the state spaces and make decorrelated updates to the actor and the critic.\n\n\n### Lectures & Readings\n\n**Required:**\n\n- David Silver's RL Course Lecture 7 - Policy Gradient Methods ([video](https://www.youtube.com/watch?v=KHZVXao4qXs), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/pg.pdf))\n\n**Optional:**\n\n- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 13: Policy Gradient Methods\n- [Deterministic Policy Gradient Algorithms](http://jmlr.org/proceedings/papers/v32/silver14.pdf)\n- [Deterministic Policy Gradient Algorithms (Talk)](http://techtalks.tv/talks/deterministic-policy-gradient-algorithms/61098/)\n- [Continuous control with deep reinforcement learning](https://arxiv.org/abs/1509.02971)\n- [Deep Deterministic Policy Gradients in TensorFlow](http://pemami4911.github.io/blog_posts/2016/08/21/ddpg-rl.html)\n- [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1602.01783)\n- [Deep Reinforcement Learning: A Tutorial (Policy Gradient Section)](http://web.archive.org/web/20161029135055/https://gym.openai.com/docs/rl#id16)\n\n\n\n### Exercises\n\n- REINFORCE with Baseline\n - Exercise\n - [Solution](CliffWalk%20REINFORCE%20with%20Baseline%20Solution.ipynb)\n- Actor-Critic with Baseline\n - Exercise\n - [Solution](CliffWalk%20Actor%20Critic%20Solution.ipynb)\n- Actor-Critic with Baseline for Continuous Action Spaces\n - Exercise\n - [Solution](Continuous%20MountainCar%20Actor%20Critic%20Solution.ipynb)\n- Deterministic Policy Gradients for Continuous Action Spaces (WIP)\n- Deep Deterministic Policy Gradients (WIP)\n- Asynchronous Advantage Actor-Critic (A3C)\n - Exercise\n - [Solution](a3c/)\n" }, { "path": "PolicyGradient/a3c/README.md", "content": "## Implementation of A3C (Asynchronous Advantage Actor-Critic)\n\n#### Running\n\n```\n./train.py --model_dir /tmp/a3c --env Breakout-v0 --t_max 5 --eval_every 300 --parallelism 8\n```\n\nSee `./train.py --help` for a full list of options. Then, monitor training progress in Tensorboard:\n\n```\ntensorboard --logdir=/tmp/a3c\n```\n\n#### Components\n\n- [`train.py`](train.py) contains the main method to start training.\n- [`estimators.py`](estimators.py) contains the Tensorflow graph definitions for the Policy and Value networks.\n- [`worker.py`](worker.py) contains code that runs in each worker threads.\n- [`policy_monitor.py`](policy_monitor.py) contains code that evaluates the policy network by running an episode and saving rewards to Tensorboard.\n" }, { "path": "PolicyGradient/a3c/estimator_test.py", "content": "import unittest\nimport gym\nimport sys\nimport os\nimport numpy as np\nimport tensorflow as tf\n\nfrom inspect import getsourcefile\ncurrent_path = os.path.dirname(os.path.abspath(getsourcefile(lambda:0)))\nimport_path = os.path.abspath(os.path.join(current_path, \"../..\"))\n\nif import_path not in sys.path:\n sys.path.append(import_path)\n\n# from lib import plotting\nfrom lib.atari.state_processor import StateProcessor\nfrom lib.atari import helpers as atari_helpers\nfrom estimators import ValueEstimator, PolicyEstimator\n\n\ndef make_env():\n return gym.envs.make(\"Breakout-v0\")\n\nVALID_ACTIONS = [0, 1, 2, 3]\n\nclass PolicyEstimatorTest(tf.test.TestCase):\n def testPredict(self):\n env = make_env()\n sp = StateProcessor()\n estimator = PolicyEstimator(len(VALID_ACTIONS))\n\n with self.test_session() as sess:\n sess.run(tf.initialize_all_variables())\n\n # Generate a state\n state = sp.process(env.reset())\n processed_state = atari_helpers.atari_make_initial_state(state)\n processed_states = np.array([processed_state])\n\n # Run feeds\n feed_dict = {\n estimator.states: processed_states,\n estimator.targets: [1.0],\n estimator.actions: [1]\n }\n loss = sess.run(estimator.loss, feed_dict)\n pred = sess.run(estimator.predictions, feed_dict)\n\n # Assertions\n self.assertTrue(loss != 0.0)\n self.assertEqual(pred[\"probs\"].shape, (1, len(VALID_ACTIONS)))\n self.assertEqual(pred[\"logits\"].shape, (1, len(VALID_ACTIONS)))\n\n def testGradient(self):\n env = make_env()\n sp = StateProcessor()\n estimator = PolicyEstimator(len(VALID_ACTIONS))\n grads = [g for g, _ in estimator.grads_and_vars]\n\n with self.test_session() as sess:\n sess.run(tf.initialize_all_variables())\n\n # Generate a state\n state = sp.process(env.reset())\n processed_state = atari_helpers.atari_make_initial_state(state)\n processed_states = np.array([processed_state])\n\n # Run feeds to get gradients\n feed_dict = {\n estimator.states: processed_states,\n estimator.targets: [1.0],\n estimator.actions: [1]\n }\n grads_ = sess.run(grads, feed_dict)\n\n # Apply calculated gradients\n grad_feed_dict = { k: v for k, v in zip(grads, grads_) }\n _ = sess.run(estimator.train_op, grad_feed_dict)\n\n\nclass ValueEstimatorTest(tf.test.TestCase):\n def testPredict(self):\n env = make_env()\n sp = StateProcessor()\n estimator = ValueEstimator()\n\n with self.test_session() as sess:\n sess.run(tf.initialize_all_variables())\n\n # Generate a state\n state = sp.process(env.reset())\n processed_state = atari_helpers.atari_make_initial_state(state)\n processed_states = np.array([processed_state])\n\n # Run feeds\n feed_dict = {\n estimator.states: processed_states,\n estimator.targets: [1.0],\n }\n loss = sess.run(estimator.loss, feed_dict)\n pred = sess.run(estimator.predictions, feed_dict)\n\n # Assertions\n self.assertTrue(loss != 0.0)\n self.assertEqual(pred[\"logits\"].shape, (1,))\n\n def testGradient(self):\n env = make_env()\n sp = StateProcessor()\n estimator = ValueEstimator()\n grads = [g for g, _ in estimator.grads_and_vars]\n\n with self.test_session() as sess:\n sess.run(tf.initialize_all_variables())\n\n # Generate a state\n state = sp.process(env.reset())\n processed_state = atari_helpers.atari_make_initial_state(state)\n processed_states = np.array([processed_state])\n\n # Run feeds\n feed_dict = {\n estimator.states: processed_states,\n estimator.targets: [1.0],\n }\n grads_ = sess.run(grads, feed_dict)\n\n # Apply calculated gradients\n grad_feed_dict = { k: v for k, v in zip(grads, grads_) }\n _ = sess.run(estimator.train_op, grad_feed_dict)\n\nif __name__ == '__main__':\n unittest.main()" }, { "path": "PolicyGradient/a3c/estimators.py", "content": "import numpy as np\nimport tensorflow as tf\n\ndef build_shared_network(X, add_summaries=False):\n \"\"\"\n Builds a 3-layer network conv -> conv -> fc as described\n in the A3C paper. This network is shared by both the policy and value net.\n\n Args:\n X: Inputs\n add_summaries: If true, add layer summaries to Tensorboard.\n\n Returns:\n Final layer activations.\n \"\"\"\n\n # Three convolutional layers\n conv1 = tf.contrib.layers.conv2d(\n X, 16, 8, 4, activation_fn=tf.nn.relu, scope=\"conv1\")\n conv2 = tf.contrib.layers.conv2d(\n conv1, 32, 4, 2, activation_fn=tf.nn.relu, scope=\"conv2\")\n\n # Fully connected layer\n fc1 = tf.contrib.layers.fully_connected(\n inputs=tf.contrib.layers.flatten(conv2),\n num_outputs=256,\n scope=\"fc1\")\n\n if add_summaries:\n tf.contrib.layers.summarize_activation(conv1)\n tf.contrib.layers.summarize_activation(conv2)\n tf.contrib.layers.summarize_activation(fc1)\n\n return fc1\n\nclass PolicyEstimator():\n \"\"\"\n Policy Function approximator. Given a observation, returns probabilities\n over all possible actions.\n\n Args:\n num_outputs: Size of the action space.\n reuse: If true, an existing shared network will be re-used.\n trainable: If true we add train ops to the network.\n Actor threads that don't update their local models and don't need\n train ops would set this to false.\n \"\"\"\n\n def __init__(self, num_outputs, reuse=False, trainable=True):\n self.num_outputs = num_outputs\n\n # Placeholders for our input\n # Our input are 4 RGB frames of shape 160, 160 each\n self.states = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.uint8, name=\"X\")\n # The TD target value\n self.targets = tf.placeholder(shape=[None], dtype=tf.float32, name=\"y\")\n # Integer id of which action was selected\n self.actions = tf.placeholder(shape=[None], dtype=tf.int32, name=\"actions\")\n\n # Normalize\n X = tf.to_float(self.states) / 255.0\n batch_size = tf.shape(self.states)[0]\n\n # Graph shared with Value Net\n with tf.variable_scope(\"shared\", reuse=reuse):\n fc1 = build_shared_network(X, add_summaries=(not reuse))\n\n\n with tf.variable_scope(\"policy_net\"):\n self.logits = tf.contrib.layers.fully_connected(fc1, num_outputs, activation_fn=None)\n self.probs = tf.nn.softmax(self.logits) + 1e-8\n\n self.predictions = {\n \"logits\": self.logits,\n \"probs\": self.probs\n }\n\n # We add entropy to the loss to encourage exploration\n self.entropy = -tf.reduce_sum(self.probs * tf.log(self.probs), 1, name=\"entropy\")\n self.entropy_mean = tf.reduce_mean(self.entropy, name=\"entropy_mean\")\n\n # Get the predictions for the chosen actions only\n gather_indices = tf.range(batch_size) * tf.shape(self.probs)[1] + self.actions\n self.picked_action_probs = tf.gather(tf.reshape(self.probs, [-1]), gather_indices)\n\n self.losses = - (tf.log(self.picked_action_probs) * self.targets + 0.01 * self.entropy)\n self.loss = tf.reduce_sum(self.losses, name=\"loss\")\n\n tf.summary.scalar(self.loss.op.name, self.loss)\n tf.summary.scalar(self.entropy_mean.op.name, self.entropy_mean)\n tf.summary.histogram(self.entropy.op.name, self.entropy)\n\n if trainable:\n # self.optimizer = tf.train.AdamOptimizer(1e-4)\n self.optimizer = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6)\n self.grads_and_vars = self.optimizer.compute_gradients(self.loss)\n self.grads_and_vars = [[grad, var] for grad, var in self.grads_and_vars if grad is not None]\n self.train_op = self.optimizer.apply_gradients(self.grads_and_vars,\n global_step=tf.contrib.framework.get_global_step())\n\n # Merge summaries from this network and the shared network (but not the value net)\n var_scope_name = tf.get_variable_scope().name\n summary_ops = tf.get_collection(tf.GraphKeys.SUMMARIES)\n sumaries = [s for s in summary_ops if \"policy_net\" in s.name or \"shared\" in s.name]\n sumaries = [s for s in summary_ops if var_scope_name in s.name]\n self.summaries = tf.summary.merge(sumaries)\n\n\nclass ValueEstimator():\n \"\"\"\n Value Function approximator. Returns a value estimator for a batch of observations.\n\n Args:\n reuse: If true, an existing shared network will be re-used.\n trainable: If true we add train ops to the network.\n Actor threads that don't update their local models and don't need\n train ops would set this to false.\n \"\"\"\n\n def __init__(self, reuse=False, trainable=True):\n # Placeholders for our input\n # Our input are 4 RGB frames of shape 160, 160 each\n self.states = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.uint8, name=\"X\")\n # The TD target value\n self.targets = tf.placeholder(shape=[None], dtype=tf.float32, name=\"y\")\n\n X = tf.to_float(self.states) / 255.0\n\n # Graph shared with Value Net\n with tf.variable_scope(\"shared\", reuse=reuse):\n fc1 = build_shared_network(X, add_summaries=(not reuse))\n\n with tf.variable_scope(\"value_net\"):\n self.logits = tf.contrib.layers.fully_connected(\n inputs=fc1,\n num_outputs=1,\n activation_fn=None)\n self.logits = tf.squeeze(self.logits, squeeze_dims=[1], name=\"logits\")\n\n self.losses = tf.squared_difference(self.logits, self.targets)\n self.loss = tf.reduce_sum(self.losses, name=\"loss\")\n\n self.predictions = {\n \"logits\": self.logits\n }\n\n # Summaries\n prefix = tf.get_variable_scope().name\n tf.summary.scalar(self.loss.name, self.loss)\n tf.summary.scalar(\"{}/max_value\".format(prefix), tf.reduce_max(self.logits))\n tf.summary.scalar(\"{}/min_value\".format(prefix), tf.reduce_min(self.logits))\n tf.summary.scalar(\"{}/mean_value\".format(prefix), tf.reduce_mean(self.logits))\n tf.summary.scalar(\"{}/reward_max\".format(prefix), tf.reduce_max(self.targets))\n tf.summary.scalar(\"{}/reward_min\".format(prefix), tf.reduce_min(self.targets))\n tf.summary.scalar(\"{}/reward_mean\".format(prefix), tf.reduce_mean(self.targets))\n tf.summary.histogram(\"{}/reward_targets\".format(prefix), self.targets)\n tf.summary.histogram(\"{}/values\".format(prefix), self.logits)\n\n if trainable:\n # self.optimizer = tf.train.AdamOptimizer(1e-4)\n self.optimizer = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6)\n self.grads_and_vars = self.optimizer.compute_gradients(self.loss)\n self.grads_and_vars = [[grad, var] for grad, var in self.grads_and_vars if grad is not None]\n self.train_op = self.optimizer.apply_gradients(self.grads_and_vars,\n global_step=tf.contrib.framework.get_global_step())\n\n var_scope_name = tf.get_variable_scope().name\n summary_ops = tf.get_collection(tf.GraphKeys.SUMMARIES)\n sumaries = [s for s in summary_ops if \"policy_net\" in s.name or \"shared\" in s.name]\n sumaries = [s for s in summary_ops if var_scope_name in s.name]\n self.summaries = tf.summary.merge(sumaries)\n" }, { "path": "PolicyGradient/a3c/policy_monitor.py", "content": "import sys\nimport os\nimport itertools\nimport collections\nimport numpy as np\nimport tensorflow as tf\nimport time\n\nfrom inspect import getsourcefile\ncurrent_path = os.path.dirname(os.path.abspath(getsourcefile(lambda:0)))\nimport_path = os.path.abspath(os.path.join(current_path, \"../..\"))\n\nif import_path not in sys.path:\n sys.path.append(import_path)\n\nfrom gym.wrappers import Monitor\nimport gym\n\nfrom lib.atari.state_processor import StateProcessor\nfrom lib.atari import helpers as atari_helpers\nfrom estimators import ValueEstimator, PolicyEstimator\nfrom worker import make_copy_params_op\n\n\nclass PolicyMonitor(object):\n \"\"\"\n Helps evaluating a policy by running an episode in an environment,\n saving a video, and plotting summaries to Tensorboard.\n\n Args:\n env: environment to run in\n policy_net: A policy estimator\n summary_writer: a tf.train.SummaryWriter used to write Tensorboard summaries\n \"\"\"\n def __init__(self, env, policy_net, summary_writer, saver=None):\n\n self.video_dir = os.path.join(summary_writer.get_logdir(), \"../videos\")\n self.video_dir = os.path.abspath(self.video_dir)\n\n self.env = Monitor(env, directory=self.video_dir, video_callable=lambda x: True, resume=True)\n self.global_policy_net = policy_net\n self.summary_writer = summary_writer\n self.saver = saver\n self.sp = StateProcessor()\n\n self.checkpoint_path = os.path.abspath(os.path.join(summary_writer.get_logdir(), \"../checkpoints/model\"))\n\n try:\n os.makedirs(self.video_dir)\n except FileExistsError:\n pass\n\n # Local policy net\n with tf.variable_scope(\"policy_eval\"):\n self.policy_net = PolicyEstimator(policy_net.num_outputs)\n\n # Op to copy params from global policy/value net parameters\n self.copy_params_op = make_copy_params_op(\n tf.contrib.slim.get_variables(scope=\"global\", collection=tf.GraphKeys.TRAINABLE_VARIABLES),\n tf.contrib.slim.get_variables(scope=\"policy_eval\", collection=tf.GraphKeys.TRAINABLE_VARIABLES))\n\n def _policy_net_predict(self, state, sess):\n feed_dict = { self.policy_net.states: [state] }\n preds = sess.run(self.policy_net.predictions, feed_dict)\n return preds[\"probs\"][0]\n\n def eval_once(self, sess):\n with sess.as_default(), sess.graph.as_default():\n # Copy params to local model\n global_step, _ = sess.run([tf.contrib.framework.get_global_step(), self.copy_params_op])\n\n # Run an episode\n done = False\n state = atari_helpers.atari_make_initial_state(self.sp.process(self.env.reset()))\n total_reward = 0.0\n episode_length = 0\n while not done:\n action_probs = self._policy_net_predict(state, sess)\n action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\n next_state, reward, done, _ = self.env.step(action)\n next_state = atari_helpers.atari_make_next_state(state, self.sp.process(next_state))\n total_reward += reward\n episode_length += 1\n state = next_state\n\n # Add summaries\n episode_summary = tf.Summary()\n episode_summary.value.add(simple_value=total_reward, tag=\"eval/total_reward\")\n episode_summary.value.add(simple_value=episode_length, tag=\"eval/episode_length\")\n self.summary_writer.add_summary(episode_summary, global_step)\n self.summary_writer.flush()\n\n if self.saver is not None:\n self.saver.save(sess, self.checkpoint_path)\n\n tf.logging.info(\"Eval results at step {}: total_reward {}, episode_length {}\".format(global_step, total_reward, episode_length))\n\n return total_reward, episode_length\n\n def continuous_eval(self, eval_every, sess, coord):\n \"\"\"\n Continuously evaluates the policy every [eval_every] seconds.\n \"\"\"\n try:\n while not coord.should_stop():\n self.eval_once(sess)\n # Sleep until next evaluation cycle\n time.sleep(eval_every)\n except tf.errors.CancelledError:\n return\n" }, { "path": "PolicyGradient/a3c/policy_monitor_test.py", "content": "import gym\nimport sys\nimport os\nimport itertools\nimport collections\nimport unittest\nimport numpy as np\nimport tensorflow as tf\nimport tempfile\n\nfrom inspect import getsourcefile\ncurrent_path = os.path.dirname(os.path.abspath(getsourcefile(lambda:0)))\nimport_path = os.path.abspath(os.path.join(current_path, \"../..\"))\n\nif import_path not in sys.path:\n sys.path.append(import_path)\n\n# from lib import plotting\nfrom lib.atari.state_processor import StateProcessor\nfrom lib.atari import helpers as atari_helpers\nfrom policy_monitor import PolicyMonitor\nfrom estimators import ValueEstimator, PolicyEstimator\n\ndef make_env():\n return gym.envs.make(\"Breakout-v0\")\n\nVALID_ACTIONS = [0, 1, 2, 3]\n\nclass PolicyMonitorTest(tf.test.TestCase):\n def setUp(self):\n super(PolicyMonitorTest, self).setUp()\n\n self.env = make_env()\n self.global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n self.summary_writer = tf.train.SummaryWriter(tempfile.mkdtemp())\n\n with tf.variable_scope(\"global\") as vs:\n self.global_policy_net = PolicyEstimator(len(VALID_ACTIONS))\n self.global_value_net = ValueEstimator(reuse=True)\n\n def testEvalOnce(self):\n pe = PolicyMonitor(\n env=self.env,\n policy_net=self.global_policy_net,\n summary_writer=self.summary_writer)\n\n with self.test_session() as sess:\n sess.run(tf.initialize_all_variables())\n total_reward, episode_length = pe.eval_once(sess)\n self.assertTrue(episode_length > 0)\n\n\nif __name__ == '__main__':\n unittest.main()" }, { "path": "PolicyGradient/a3c/train.py", "content": "#! /usr/bin/env python\n\nimport unittest\nimport gym\nimport sys\nimport os\nimport numpy as np\nimport tensorflow as tf\nimport itertools\nimport shutil\nimport threading\nimport multiprocessing\n\nfrom inspect import getsourcefile\ncurrent_path = os.path.dirname(os.path.abspath(getsourcefile(lambda:0)))\nimport_path = os.path.abspath(os.path.join(current_path, \"../..\"))\n\nif import_path not in sys.path:\n sys.path.append(import_path)\n\nfrom lib.atari import helpers as atari_helpers\nfrom estimators import ValueEstimator, PolicyEstimator\nfrom policy_monitor import PolicyMonitor\nfrom worker import Worker\n\n\ntf.flags.DEFINE_string(\"model_dir\", \"/tmp/a3c\", \"Directory to write Tensorboard summaries and videos to.\")\ntf.flags.DEFINE_string(\"env\", \"Breakout-v0\", \"Name of gym Atari environment, e.g. Breakout-v0\")\ntf.flags.DEFINE_integer(\"t_max\", 5, \"Number of steps before performing an update\")\ntf.flags.DEFINE_integer(\"max_global_steps\", None, \"Stop training after this many steps in the environment. Defaults to running indefinitely.\")\ntf.flags.DEFINE_integer(\"eval_every\", 300, \"Evaluate the policy every N seconds\")\ntf.flags.DEFINE_boolean(\"reset\", False, \"If set, delete the existing model directory and start training from scratch.\")\ntf.flags.DEFINE_integer(\"parallelism\", None, \"Number of threads to run. If not set we run [num_cpu_cores] threads.\")\n\nFLAGS = tf.flags.FLAGS\n\ndef make_env(wrap=True):\n env = gym.envs.make(FLAGS.env)\n # remove the timelimitwrapper\n env = env.env\n if wrap:\n env = atari_helpers.AtariEnvWrapper(env)\n return env\n\n# Depending on the game we may have a limited action space\nenv_ = make_env()\nif FLAGS.env == \"Pong-v0\" or FLAGS.env == \"Breakout-v0\":\n VALID_ACTIONS = list(range(4))\nelse:\n VALID_ACTIONS = list(range(env_.action_space.n))\nenv_.close()\n\n\n# Set the number of workers\nNUM_WORKERS = multiprocessing.cpu_count()\nif FLAGS.parallelism:\n NUM_WORKERS = FLAGS.parallelism\n\nMODEL_DIR = FLAGS.model_dir\nCHECKPOINT_DIR = os.path.join(MODEL_DIR, \"checkpoints\")\n\n# Optionally empty model directory\nif FLAGS.reset:\n shutil.rmtree(MODEL_DIR, ignore_errors=True)\n\nif not os.path.exists(CHECKPOINT_DIR):\n os.makedirs(CHECKPOINT_DIR)\n\nsummary_writer = tf.summary.FileWriter(os.path.join(MODEL_DIR, \"train\"))\n\nwith tf.device(\"/cpu:0\"):\n\n # Keeps track of the number of updates we've performed\n global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n\n # Global policy and value nets\n with tf.variable_scope(\"global\") as vs:\n policy_net = PolicyEstimator(num_outputs=len(VALID_ACTIONS))\n value_net = ValueEstimator(reuse=True)\n\n # Global step iterator\n global_counter = itertools.count()\n\n # Create worker graphs\n workers = []\n for worker_id in range(NUM_WORKERS):\n # We only write summaries in one of the workers because they're\n # pretty much identical and writing them on all workers\n # would be a waste of space\n worker_summary_writer = None\n if worker_id == 0:\n worker_summary_writer = summary_writer\n\n worker = Worker(\n name=\"worker_{}\".format(worker_id),\n env=make_env(),\n policy_net=policy_net,\n value_net=value_net,\n global_counter=global_counter,\n discount_factor = 0.99,\n summary_writer=worker_summary_writer,\n max_global_steps=FLAGS.max_global_steps)\n workers.append(worker)\n\n saver = tf.train.Saver(keep_checkpoint_every_n_hours=2.0, max_to_keep=10)\n\n # Used to occasionally save videos for our policy net\n # and write episode rewards to Tensorboard\n pe = PolicyMonitor(\n env=make_env(wrap=False),\n policy_net=policy_net,\n summary_writer=summary_writer,\n saver=saver)\n\nwith tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n coord = tf.train.Coordinator()\n\n # Load a previous checkpoint if it exists\n latest_checkpoint = tf.train.latest_checkpoint(CHECKPOINT_DIR)\n if latest_checkpoint:\n print(\"Loading model checkpoint: {}\".format(latest_checkpoint))\n saver.restore(sess, latest_checkpoint)\n\n # Start worker threads\n worker_threads = []\n for worker in workers:\n worker_fn = lambda worker=worker: worker.run(sess, coord, FLAGS.t_max)\n t = threading.Thread(target=worker_fn)\n t.start()\n worker_threads.append(t)\n\n # Start a thread for policy eval task\n monitor_thread = threading.Thread(target=lambda: pe.continuous_eval(FLAGS.eval_every, sess, coord))\n monitor_thread.start()\n\n # Wait for all workers to finish\n coord.join(worker_threads)\n" }, { "path": "PolicyGradient/a3c/worker.py", "content": "import gym\nimport sys\nimport os\nimport itertools\nimport collections\nimport numpy as np\nimport tensorflow as tf\n\nfrom inspect import getsourcefile\ncurrent_path = os.path.dirname(os.path.abspath(getsourcefile(lambda:0)))\nimport_path = os.path.abspath(os.path.join(current_path, \"../..\"))\n\nif import_path not in sys.path:\n sys.path.append(import_path)\n\n# from lib import plotting\nfrom lib.atari.state_processor import StateProcessor\nfrom lib.atari import helpers as atari_helpers\nfrom estimators import ValueEstimator, PolicyEstimator\n\nTransition = collections.namedtuple(\"Transition\", [\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n\n\ndef make_copy_params_op(v1_list, v2_list):\n \"\"\"\n Creates an operation that copies parameters from variable in v1_list to variables in v2_list.\n The ordering of the variables in the lists must be identical.\n \"\"\"\n v1_list = list(sorted(v1_list, key=lambda v: v.name))\n v2_list = list(sorted(v2_list, key=lambda v: v.name))\n\n update_ops = []\n for v1, v2 in zip(v1_list, v2_list):\n op = v2.assign(v1)\n update_ops.append(op)\n\n return update_ops\n\ndef make_train_op(local_estimator, global_estimator):\n \"\"\"\n Creates an op that applies local estimator gradients\n to the global estimator.\n \"\"\"\n local_grads, _ = zip(*local_estimator.grads_and_vars)\n # Clip gradients\n local_grads, _ = tf.clip_by_global_norm(local_grads, 5.0)\n _, global_vars = zip(*global_estimator.grads_and_vars)\n local_global_grads_and_vars = list(zip(local_grads, global_vars))\n return global_estimator.optimizer.apply_gradients(local_global_grads_and_vars,\n global_step=tf.contrib.framework.get_global_step())\n\n\nclass Worker(object):\n \"\"\"\n An A3C worker thread. Runs episodes locally and updates global shared value and policy nets.\n\n Args:\n name: A unique name for this worker\n env: The Gym environment used by this worker\n policy_net: Instance of the globally shared policy net\n value_net: Instance of the globally shared value net\n global_counter: Iterator that holds the global step\n discount_factor: Reward discount factor\n summary_writer: A tf.train.SummaryWriter for Tensorboard summaries\n max_global_steps: If set, stop coordinator when global_counter > max_global_steps\n \"\"\"\n def __init__(self, name, env, policy_net, value_net, global_counter, discount_factor=0.99, summary_writer=None, max_global_steps=None):\n self.name = name\n self.discount_factor = discount_factor\n self.max_global_steps = max_global_steps\n self.global_step = tf.contrib.framework.get_global_step()\n self.global_policy_net = policy_net\n self.global_value_net = value_net\n self.global_counter = global_counter\n self.local_counter = itertools.count()\n self.sp = StateProcessor()\n self.summary_writer = summary_writer\n self.env = env\n\n # Create local policy/value nets that are not updated asynchronously\n with tf.variable_scope(name):\n self.policy_net = PolicyEstimator(policy_net.num_outputs)\n self.value_net = ValueEstimator(reuse=True)\n\n # Op to copy params from global policy/valuenets\n self.copy_params_op = make_copy_params_op(\n tf.contrib.slim.get_variables(scope=\"global\", collection=tf.GraphKeys.TRAINABLE_VARIABLES),\n tf.contrib.slim.get_variables(scope=self.name+'/', collection=tf.GraphKeys.TRAINABLE_VARIABLES))\n\n self.vnet_train_op = make_train_op(self.value_net, self.global_value_net)\n self.pnet_train_op = make_train_op(self.policy_net, self.global_policy_net)\n\n self.state = None\n\n def run(self, sess, coord, t_max):\n with sess.as_default(), sess.graph.as_default():\n # Initial state\n self.state = atari_helpers.atari_make_initial_state(self.sp.process(self.env.reset()))\n try:\n while not coord.should_stop():\n # Copy Parameters from the global networks\n sess.run(self.copy_params_op)\n\n # Collect some experience\n transitions, local_t, global_t = self.run_n_steps(t_max, sess)\n\n if self.max_global_steps is not None and global_t >= self.max_global_steps:\n tf.logging.info(\"Reached global step {}. Stopping.\".format(global_t))\n coord.request_stop()\n return\n\n # Update the global networks\n self.update(transitions, sess)\n\n except tf.errors.CancelledError:\n return\n\n def _policy_net_predict(self, state, sess):\n feed_dict = { self.policy_net.states: [state] }\n preds = sess.run(self.policy_net.predictions, feed_dict)\n return preds[\"probs\"][0]\n\n def _value_net_predict(self, state, sess):\n feed_dict = { self.value_net.states: [state] }\n preds = sess.run(self.value_net.predictions, feed_dict)\n return preds[\"logits\"][0]\n\n def run_n_steps(self, n, sess):\n transitions = []\n for _ in range(n):\n # Take a step\n action_probs = self._policy_net_predict(self.state, sess)\n action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\n next_state, reward, done, _ = self.env.step(action)\n next_state = atari_helpers.atari_make_next_state(self.state, self.sp.process(next_state))\n\n # Store transition\n transitions.append(Transition(\n state=self.state, action=action, reward=reward, next_state=next_state, done=done))\n\n # Increase local and global counters\n local_t = next(self.local_counter)\n global_t = next(self.global_counter)\n\n if local_t % 100 == 0:\n tf.logging.info(\"{}: local Step {}, global step {}\".format(self.name, local_t, global_t))\n\n if done:\n self.state = atari_helpers.atari_make_initial_state(self.sp.process(self.env.reset()))\n break\n else:\n self.state = next_state\n return transitions, local_t, global_t\n\n def update(self, transitions, sess):\n \"\"\"\n Updates global policy and value networks based on collected experience\n\n Args:\n transitions: A list of experience transitions\n sess: A Tensorflow session\n \"\"\"\n\n # If we episode was not done we bootstrap the value from the last state\n reward = 0.0\n if not transitions[-1].done:\n reward = self._value_net_predict(transitions[-1].next_state, sess)\n\n # Accumulate minibatch exmaples\n states = []\n policy_targets = []\n value_targets = []\n actions = []\n\n for transition in transitions[::-1]:\n reward = transition.reward + self.discount_factor * reward\n policy_target = (reward - self._value_net_predict(transition.state, sess))\n # Accumulate updates\n states.append(transition.state)\n actions.append(transition.action)\n policy_targets.append(policy_target)\n value_targets.append(reward)\n\n feed_dict = {\n self.policy_net.states: np.array(states),\n self.policy_net.targets: policy_targets,\n self.policy_net.actions: actions,\n self.value_net.states: np.array(states),\n self.value_net.targets: value_targets,\n }\n\n # Train the global estimators using local gradients\n global_step, pnet_loss, vnet_loss, _, _, pnet_summaries, vnet_summaries = sess.run([\n self.global_step,\n self.policy_net.loss,\n self.value_net.loss,\n self.pnet_train_op,\n self.vnet_train_op,\n self.policy_net.summaries,\n self.value_net.summaries\n ], feed_dict)\n\n # Write summaries\n if self.summary_writer is not None:\n self.summary_writer.add_summary(pnet_summaries, global_step)\n self.summary_writer.add_summary(vnet_summaries, global_step)\n self.summary_writer.flush()\n\n return pnet_loss, vnet_loss, pnet_summaries, vnet_summaries\n" }, { "path": "PolicyGradient/a3c/worker_test.py", "content": "import gym\nimport sys\nimport os\nimport itertools\nimport collections\nimport unittest\nimport numpy as np\nimport tensorflow as tf\n\nfrom inspect import getsourcefile\ncurrent_path = os.path.dirname(os.path.abspath(getsourcefile(lambda:0)))\nimport_path = os.path.abspath(os.path.join(current_path, \"../..\"))\n\nif import_path not in sys.path:\n sys.path.append(import_path)\n\n# from lib import plotting\nfrom lib.atari.state_processor import StateProcessor\nfrom lib.atari import helpers as atari_helpers\nfrom worker import Worker\nfrom estimators import ValueEstimator, PolicyEstimator\n\ndef make_env():\n return gym.envs.make(\"Breakout-v0\")\n\nVALID_ACTIONS = [0, 1, 2, 3]\n\nclass WorkerTest(tf.test.TestCase):\n def setUp(self):\n super(WorkerTest, self).setUp()\n\n self.env = make_env()\n self.discount_factor = 0.99\n self.global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n self.global_counter = itertools.count()\n self.sp = StateProcessor()\n\n with tf.variable_scope(\"global\") as vs:\n self.global_policy_net = PolicyEstimator(len(VALID_ACTIONS))\n self.global_value_net = ValueEstimator(reuse=True)\n\n def testPolicyNetPredict(self):\n w = Worker(\n name=\"test\",\n env=make_env(),\n policy_net=self.global_policy_net,\n value_net=self.global_value_net,\n global_counter=self.global_counter,\n discount_factor=self.discount_factor)\n\n with self.test_session() as sess:\n sess.run(tf.initialize_all_variables())\n state = self.sp.process(self.env.reset())\n processed_state = atari_helpers.atari_make_initial_state(state)\n action_values = w._policy_net_predict(processed_state, sess)\n self.assertEqual(action_values.shape, (4,))\n\n\n def testValueNetPredict(self):\n w = Worker(\n name=\"test\",\n env=make_env(),\n policy_net=self.global_policy_net,\n value_net=self.global_value_net,\n global_counter=self.global_counter,\n discount_factor=self.discount_factor)\n\n with self.test_session() as sess:\n sess.run(tf.initialize_all_variables())\n state = self.sp.process(self.env.reset())\n processed_state = atari_helpers.atari_make_initial_state(state)\n state_value = w._value_net_predict(processed_state, sess)\n self.assertEqual(state_value.shape, ())\n\n def testRunNStepsAndUpdate(self):\n w = Worker(\n name=\"test\",\n env=make_env(),\n policy_net=self.global_policy_net,\n value_net=self.global_value_net,\n global_counter=self.global_counter,\n discount_factor=self.discount_factor)\n\n with self.test_session() as sess:\n sess.run(tf.initialize_all_variables())\n state = self.sp.process(self.env.reset())\n processed_state = atari_helpers.atari_make_initial_state(state)\n w.state = processed_state\n transitions, local_t, global_t = w.run_n_steps(10, sess)\n policy_net_loss, value_net_loss, policy_net_summaries, value_net_summaries = w.update(transitions, sess)\n\n self.assertEqual(len(transitions), 10)\n self.assertIsNotNone(policy_net_loss)\n self.assertIsNotNone(value_net_loss)\n self.assertIsNotNone(policy_net_summaries)\n self.assertIsNotNone(value_net_summaries)\n\n\nif __name__ == '__main__':\n unittest.main()" }, { "path": "README.md", "content": "### Overview\n\nThis repository provides code, exercises and solutions for popular Reinforcement Learning algorithms. These are meant to serve as a learning tool to complement the theoretical materials from\n\n- [Reinforcement Learning: An Introduction (2nd Edition)](http://incompleteideas.net/book/RLbook2018.pdf)\n- [David Silver's Reinforcement Learning Course](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)\n\nEach folder in corresponds to one or more chapters of the above textbook and/or course. In addition to exercises and solution, each folder also contains a list of learning goals, a brief concept summary, and links to the relevant readings.\n\nAll code is written in Python 3 and uses RL environments from [OpenAI Gym](https://gym.openai.com/). Advanced techniques use [Tensorflow](https://www.tensorflow.org/) for neural network implementations.\n\n\n### Table of Contents\n\n- [Introduction to RL problems & OpenAI Gym](Introduction/)\n- [MDPs and Bellman Equations](MDP/)\n- [Dynamic Programming: Model-Based RL, Policy Iteration and Value Iteration](DP/)\n- [Monte Carlo Model-Free Prediction & Control](MC/)\n- [Temporal Difference Model-Free Prediction & Control](TD/)\n- [Function Approximation](FA/)\n- [Deep Q Learning](DQN/) (WIP)\n- [Policy Gradient Methods](PolicyGradient/) (WIP)\n- Learning and Planning (WIP)\n- Exploration and Exploitation (WIP)\n\n\n### List of Implemented Algorithms\n\n- [Dynamic Programming Policy Evaluation](DP/Policy%20Evaluation%20Solution.ipynb)\n- [Dynamic Programming Policy Iteration](DP/Policy%20Iteration%20Solution.ipynb)\n- [Dynamic Programming Value Iteration](DP/Value%20Iteration%20Solution.ipynb)\n- [Monte Carlo Prediction](MC/MC%20Prediction%20Solution.ipynb)\n- [Monte Carlo Control with Epsilon-Greedy Policies](MC/MC%20Control%20with%20Epsilon-Greedy%20Policies%20Solution.ipynb)\n- [Monte Carlo Off-Policy Control with Importance Sampling](MC/Off-Policy%20MC%20Control%20with%20Weighted%20Importance%20Sampling%20Solution.ipynb)\n- [SARSA (On Policy TD Learning)](TD/SARSA%20Solution.ipynb)\n- [Q-Learning (Off Policy TD Learning)](TD/Q-Learning%20Solution.ipynb)\n- [Q-Learning with Linear Function Approximation](FA/Q-Learning%20with%20Value%20Function%20Approximation%20Solution.ipynb)\n- [Deep Q-Learning for Atari Games](DQN/Deep%20Q%20Learning%20Solution.ipynb)\n- [Double Deep-Q Learning for Atari Games](DQN/Double%20DQN%20Solution.ipynb)\n- Deep Q-Learning with Prioritized Experience Replay (WIP)\n- [Policy Gradient: REINFORCE with Baseline](PolicyGradient/CliffWalk%20REINFORCE%20with%20Baseline%20Solution.ipynb)\n- [Policy Gradient: Actor Critic with Baseline](PolicyGradient/CliffWalk%20Actor%20Critic%20Solution.ipynb)\n- [Policy Gradient: Actor Critic with Baseline for Continuous Action Spaces](PolicyGradient/Continuous%20MountainCar%20Actor%20Critic%20Solution.ipynb)\n- Deterministic Policy Gradients for Continuous Action Spaces (WIP)\n- Deep Deterministic Policy Gradients (DDPG) (WIP)\n- [Asynchronous Advantage Actor Critic (A3C)](PolicyGradient/a3c)\n\n\n### Resources\n\nTextbooks:\n\n- [Reinforcement Learning: An Introduction (2nd Edition)](http://incompleteideas.net/book/RLbook2018.pdf)\n\nClasses:\n\n- [David Silver's Reinforcement Learning Course (UCL, 2015)](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)\n- [CS294 - Deep Reinforcement Learning (Berkeley, Fall 2015)](http://rll.berkeley.edu/deeprlcourse/)\n- [CS 8803 - Reinforcement Learning (Georgia Tech)](https://www.udacity.com/course/reinforcement-learning--ud600)\n- [CS885 - Reinforcement Learning (UWaterloo), Spring 2018](https://cs.uwaterloo.ca/~ppoupart/teaching/cs885-spring18/)\n- [CS294-112 - Deep Reinforcement Learning (UC Berkeley)](http://rail.eecs.berkeley.edu/deeprlcourse/)\n\nTalks/Tutorials:\n\n- [Introduction to Reinforcement Learning (Joelle Pineau @ Deep Learning Summer School 2016)](http://videolectures.net/deeplearning2016_pineau_reinforcement_learning/)\n- [Deep Reinforcement Learning (Pieter Abbeel @ Deep Learning Summer School 2016)](http://videolectures.net/deeplearning2016_abbeel_deep_reinforcement/)\n- [Deep Reinforcement Learning ICML 2016 Tutorial (David Silver)](http://techtalks.tv/talks/deep-reinforcement-learning/62360/)\n- [Tutorial: Introduction to Reinforcement Learning with Function Approximation](https://www.youtube.com/watch?v=ggqnxyjaKe4)\n- [John Schulman - Deep Reinforcement Learning (4 Lectures)](https://www.youtube.com/playlist?list=PLjKEIQlKCTZYN3CYBlj8r58SbNorobqcp)\n- [Deep Reinforcement Learning Slides @ NIPS 2016](http://people.eecs.berkeley.edu/~pabbeel/nips-tutorial-policy-optimization-Schulman-Abbeel.pdf)\n- [OpenAI Spinning Up](https://spinningup.openai.com/en/latest/user/introduction.html)\n- [Advanced Deep Learning & Reinforcement Learning (UCL 2018, DeepMind)](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs)\n-[Deep RL Bootcamp](https://sites.google.com/view/deep-rl-bootcamp/lectures)\n\nOther Projects:\n\n- [carpedm20/deep-rl-tensorflow](https://github.com/carpedm20/deep-rl-tensorflow)\n- [matthiasplappert/keras-rl](https://github.com/matthiasplappert/keras-rl)\n\nSelected Papers:\n\n- [Human-Level Control through Deep Reinforcement Learning (2015-02)](http://www.readcube.com/articles/10.1038/nature14236)\n- [Deep Reinforcement Learning with Double Q-learning (2015-09)](http://arxiv.org/abs/1509.06461)\n- [Continuous control with deep reinforcement learning (2015-09)](https://arxiv.org/abs/1509.02971)\n- [Prioritized Experience Replay (2015-11)](http://arxiv.org/abs/1511.05952)\n- [Dueling Network Architectures for Deep Reinforcement Learning (2015-11)](http://arxiv.org/abs/1511.06581)\n- [Asynchronous Methods for Deep Reinforcement Learning (2016-02)](http://arxiv.org/abs/1602.01783)\n- [Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (2016-03)](http://arxiv.org/abs/1603.01121)\n- [Mastering the game of Go with deep neural networks and tree search](https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf)\n" }, { "path": "TD/Cliff Environment Playground.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import gym\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"\\n\",\n \"from lib.envs.cliff_walking import CliffWalkingEnv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"36\\n\",\n \"o o o o o o o o o o o o\\n\",\n \"o o o o o o o o o o o o\\n\",\n \"o o o o o o o o o o o o\\n\",\n \"x C C C C C C C C C C T\\n\",\n \"\\n\",\n \"(24, -1.0, False, {'prob': 1.0})\\n\",\n \"o o o o o o o o o o o o\\n\",\n \"o o o o o o o o o o o o\\n\",\n \"x o o o o o o o o o o o\\n\",\n \"o C C C C C C C C C C T\\n\",\n \"\\n\",\n \"(25, -1.0, False, {'prob': 1.0})\\n\",\n \"o o o o o o o o o o o o\\n\",\n \"o o o o o o o o o o o o\\n\",\n \"o x o o o o o o o o o o\\n\",\n \"o C C C C C C C C C C T\\n\",\n \"\\n\",\n \"(26, -1.0, False, {'prob': 1.0})\\n\",\n \"o o o o o o o o o o o o\\n\",\n \"o o o o o o o o o o o o\\n\",\n \"o o x o o o o o o o o o\\n\",\n \"o C C C C C C C C C C T\\n\",\n \"\\n\",\n \"(38, -100.0, True, {'prob': 1.0})\\n\",\n \"o o o o o o o o o o o o\\n\",\n \"o o o o o o o o o o o o\\n\",\n \"o o o o o o o o o o o o\\n\",\n \"o C x C C C C C C C C T\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"env = CliffWalkingEnv()\\n\",\n \"\\n\",\n \"print(env.reset())\\n\",\n \"env.render()\\n\",\n \"\\n\",\n \"print(env.step(0))\\n\",\n \"env.render()\\n\",\n \"\\n\",\n \"print(env.step(1))\\n\",\n \"env.render()\\n\",\n \"\\n\",\n \"print(env.step(1))\\n\",\n \"env.render()\\n\",\n \"\\n\",\n \"print(env.step(2))\\n\",\n \"env.render()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "TD/Q-Learning Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import itertools\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"\\n\",\n \"from collections import defaultdict\\n\",\n \"from lib.envs.cliff_walking import CliffWalkingEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"env = CliffWalkingEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def make_epsilon_greedy_policy(Q, epsilon, nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates an epsilon-greedy policy based on a given Q-function and epsilon.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" Q: A dictionary that maps from state -> action-values.\\n\",\n \" Each value is a numpy array of length nA (see below)\\n\",\n \" epsilon: The probability to select a random action. Float between 0 and 1.\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A function that takes the observation as an argument and returns\\n\",\n \" the probabilities for each action in the form of a numpy array of length nA.\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def policy_fn(observation):\\n\",\n \" A = np.ones(nA, dtype=float) * epsilon / nA\\n\",\n \" best_action = np.argmax(Q[observation])\\n\",\n \" A[best_action] += (1.0 - epsilon)\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def q_learning(env, num_episodes, discount_factor=1.0, alpha=0.5, epsilon=0.1):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Q-Learning algorithm: Off-policy TD control. Finds the optimal greedy policy\\n\",\n \" while following an epsilon-greedy policy\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI environment.\\n\",\n \" num_episodes: Number of episodes to run for.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" alpha: TD learning rate.\\n\",\n \" epsilon: Chance to sample a random action. Float between 0 and 1.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A tuple (Q, episode_lengths).\\n\",\n \" Q is the optimal action-value function, a dictionary mapping state -> action values.\\n\",\n \" stats is an EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # The final action-value function.\\n\",\n \" # A nested dictionary that maps state -> (action -> action-value).\\n\",\n \" Q = defaultdict(lambda: np.zeros(env.action_space.n))\\n\",\n \"\\n\",\n \" # Keeps track of useful statistics\\n\",\n \" stats = plotting.EpisodeStats(\\n\",\n \" episode_lengths=np.zeros(num_episodes),\\n\",\n \" episode_rewards=np.zeros(num_episodes)) \\n\",\n \" \\n\",\n \" # The policy we're following\\n\",\n \" policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\\n\",\n \" \\n\",\n \" for i_episode in range(num_episodes):\\n\",\n \" # Print out which episode we're on, useful for debugging.\\n\",\n \" if (i_episode + 1) % 100 == 0:\\n\",\n \" print(\\\"\\\\rEpisode {}/{}.\\\".format(i_episode + 1, num_episodes), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \" \\n\",\n \" # Reset the environment and pick the first action\\n\",\n \" state = env.reset()\\n\",\n \" \\n\",\n \" # One step in the environment\\n\",\n \" # total_reward = 0.0\\n\",\n \" for t in itertools.count():\\n\",\n \" \\n\",\n \" # Take a step\\n\",\n \" action_probs = policy(state)\\n\",\n \" action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\\n\",\n \" next_state, reward, done, _ = env.step(action)\\n\",\n \"\\n\",\n \" # Update statistics\\n\",\n \" stats.episode_rewards[i_episode] += reward\\n\",\n \" stats.episode_lengths[i_episode] = t\\n\",\n \" \\n\",\n \" # TD Update\\n\",\n \" best_next_action = np.argmax(Q[next_state]) \\n\",\n \" td_target = reward + discount_factor * Q[next_state][best_next_action]\\n\",\n \" td_delta = td_target - Q[state][action]\\n\",\n \" Q[state][action] += alpha * td_delta\\n\",\n \" \\n\",\n \" if done:\\n\",\n \" break\\n\",\n \" \\n\",\n \" state = next_state\\n\",\n \" \\n\",\n \" return Q, stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Episode 500/500.\"\n ]\n }\n ],\n \"source\": [\n \"Q, stats = q_learning(env, 500)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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gvev39/Zl4AZZzdbofD4ejuYVAKeO1yG69fbuP1y12DBg3qUiCetfYj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\\n/x+sHVZ0AeaDBAAAAABJRU5ErkJggg==\\n\",\n 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gzw6/c43uFu3EuQQYMG4a+//sK+ffuUv3fffdfpmAEDBjgFFw8ePBiV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V25+GOHQVw83qXX4hqFvxZyeTnY4wt+TUT3qRMRERGJBuYXYh++i732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\\n6kRERERM4A+K6gZm5bkn4gAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plotting.plot_episode_stats(stats)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "TD/Q-Learning.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import itertools\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"\\n\",\n \"from collections import defaultdict\\n\",\n \"from lib.envs.cliff_walking import CliffWalkingEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"env = CliffWalkingEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def make_epsilon_greedy_policy(Q, epsilon, nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates an epsilon-greedy policy based on a given Q-function and epsilon.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" Q: A dictionary that maps from state -> action-values.\\n\",\n \" Each value is a numpy array of length nA (see below)\\n\",\n \" epsilon: The probability to select a random action. Float between 0 and 1.\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A function that takes the observation as an argument and returns\\n\",\n \" the probabilities for each action in the form of a numpy array of length nA.\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def policy_fn(observation):\\n\",\n \" A = np.ones(nA, dtype=float) * epsilon / nA\\n\",\n \" best_action = np.argmax(Q[observation])\\n\",\n \" A[best_action] += (1.0 - epsilon)\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def q_learning(env, num_episodes, discount_factor=1.0, alpha=0.5, epsilon=0.1):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Q-Learning algorithm: Off-policy TD control. Finds the optimal greedy policy\\n\",\n \" while following an epsilon-greedy policy\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI environment.\\n\",\n \" num_episodes: Number of episodes to run for.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" alpha: TD learning rate.\\n\",\n \" epsilon: Chance to sample a random action. Float between 0 and 1.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A tuple (Q, episode_lengths).\\n\",\n \" Q is the optimal action-value function, a dictionary mapping state -> action values.\\n\",\n \" stats is an EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # The final action-value function.\\n\",\n \" # A nested dictionary that maps state -> (action -> action-value).\\n\",\n \" Q = defaultdict(lambda: np.zeros(env.action_space.n))\\n\",\n \"\\n\",\n \" # Keeps track of useful statistics\\n\",\n \" stats = plotting.EpisodeStats(\\n\",\n \" episode_lengths=np.zeros(num_episodes),\\n\",\n \" episode_rewards=np.zeros(num_episodes)) \\n\",\n \" \\n\",\n \" # The policy we're following\\n\",\n \" policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\\n\",\n \" \\n\",\n \" for i_episode in range(num_episodes):\\n\",\n \" # Print out which episode we're on, useful for debugging.\\n\",\n \" if (i_episode + 1) % 100 == 0:\\n\",\n \" print(\\\"\\\\rEpisode {}/{}.\\\".format(i_episode + 1, num_episodes), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \" \\n\",\n \" # Implement this!\\n\",\n \" \\n\",\n \" return Q, stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Episode 500/500.\"\n ]\n }\n ],\n \"source\": [\n \"Q, stats = q_learning(env, 500)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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tQY/HJzc312k56/kz2CD70LbvQvuNG/4EXvgltqaqoyMzO904mJiT73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\\nAAAsguAHAABgEQQ/AAAAi/h/v40/Klo6ZhYAAAAASUVORK5CYII=\\n\",\n 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Pfoo49K//79K4z39fU12TaJiIwaNUratWtn1vKoajzUa+Pat2+P3bt3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\\njPxL+yp/oeV0OnnnnXcYHBxk//79XLlyZd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AAAbI5ABwAAYHMEOgAAAJsj0AEAANgcgQ4AAMDmCHQAAAA2R6ADAACw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\\nsDkCHQAAgM39P+XhbrHG6s8eAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plotting.plot_episode_stats(stats)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "TD/README.md", "content": "## Model-Free Prediction & Control with Temporal Difference (TD) and Q-Learning\n\n\n### Learning Goals\n\n- Understand TD(0) for prediction\n- Understand SARSA for on-policy control\n- Understand Q-Learning for off-policy control\n- Understand the benefits of TD algorithms over MC and DP approaches\n- Understand how n-step methods unify MC and TD approaches\n- Understand the backward and forward view of TD-Lambda\n\n\n### Summary\n\n- TD-Learning is a combination of Monte Carlo and Dynamic Programming ideas. Like Monte Carlo, TD works based on samples and doesn't require a model of the environment. Like Dynamic Programming, TD uses bootstrapping to make updates.\n- Whether MC or TD is better depends on the problem and there are no theoretical results that prove a clear winner.\n- General Update Rule: `Q[s,a] += learning_rate * (td_target - Q[s,a])`. `td_target - Q[s,a]` is also called the TD Error.\n- SARSA: On-Policy TD Control\n- TD Target for SARSA: `R[t+1] + discount_factor * Q[next_state][next_action]`\n- Q-Learning: Off-policy TD Control\n- TD Target for Q-Learning: `R[t+1] + discount_factor * max(Q[next_state])`\n- Q-Learning has a positive bias because it uses the maximum of estimated Q values to estimate the maximum action value, all from the same experience. Double Q-Learning gets around this by splitting the experience and using different Q functions for maximization and estimation.\n- N-Step methods unify MC and TD approaches. They making updates based on n-steps instead of a single step (TD-0) or a full episode (MC).\n\n\n### Lectures & Readings\n\n**Required:**\n\n- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 6: Temporal-Difference Learning\n- David Silver's RL Course Lecture 4 - Model-Free Prediction ([video](https://www.youtube.com/watch?v=PnHCvfgC_ZA), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MC-TD.pdf))\n- David Silver's RL Course Lecture 5 - Model-Free Control ([video](https://www.youtube.com/watch?v=0g4j2k_Ggc4), [slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/control.pdf))\n\n**Optional:**\n\n- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 7: Multi-Step Bootstrapping\n- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/RLbook2018.pdf) - Chapter 12: Eligibility Traces\n\n\n### Exercises\n\n- Get familiar with the [Windy Gridworld Playground](Windy%20Gridworld%20Playground.ipynb)\n- Implement SARSA\n - [Exercise](SARSA.ipynb)\n - [Solution](SARSA%20Solution.ipynb)\n- Get familiar with the [Cliff Environment Playground](Cliff%20Environment%20Playground.ipynb)\n- Implement Q-Learning in Python\n - [Exercise](Q-Learning.ipynb)\n - [Solution](Q-Learning%20Solution.ipynb)\n" }, { "path": "TD/SARSA Solution.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import itertools\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"\\n\",\n \"from collections import defaultdict\\n\",\n \"from lib.envs.windy_gridworld import WindyGridworldEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"env = WindyGridworldEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def make_epsilon_greedy_policy(Q, epsilon, nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates an epsilon-greedy policy based on a given Q-function and epsilon.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" Q: A dictionary that maps from state -> action-values.\\n\",\n \" Each value is a numpy array of length nA (see below)\\n\",\n \" epsilon: The probability to select a random action . float between 0 and 1.\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A function that takes the observation as an argument and returns\\n\",\n \" the probabilities for each action in the form of a numpy array of length nA.\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def policy_fn(observation):\\n\",\n \" A = np.ones(nA, dtype=float) * epsilon / nA\\n\",\n \" best_action = np.argmax(Q[observation])\\n\",\n \" A[best_action] += (1.0 - epsilon)\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def sarsa(env, num_episodes, discount_factor=1.0, alpha=0.5, epsilon=0.1):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" SARSA algorithm: On-policy TD control. Finds the optimal epsilon-greedy policy.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI environment.\\n\",\n \" num_episodes: Number of episodes to run for.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" alpha: TD learning rate.\\n\",\n \" epsilon: Chance the sample a random action. Float betwen 0 and 1.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A tuple (Q, stats).\\n\",\n \" Q is the optimal action-value function, a dictionary mapping state -> action values.\\n\",\n \" stats is an EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # The final action-value function.\\n\",\n \" # A nested dictionary that maps state -> (action -> action-value).\\n\",\n \" Q = defaultdict(lambda: np.zeros(env.action_space.n))\\n\",\n \" \\n\",\n \" # Keeps track of useful statistics\\n\",\n \" stats = plotting.EpisodeStats(\\n\",\n \" episode_lengths=np.zeros(num_episodes),\\n\",\n \" episode_rewards=np.zeros(num_episodes))\\n\",\n \"\\n\",\n \" # The policy we're following\\n\",\n \" policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\\n\",\n \" \\n\",\n \" for i_episode in range(num_episodes):\\n\",\n \" # Print out which episode we're on, useful for debugging.\\n\",\n \" if (i_episode + 1) % 100 == 0:\\n\",\n \" print(\\\"\\\\rEpisode {}/{}.\\\".format(i_episode + 1, num_episodes), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \" \\n\",\n \" # Reset the environment and pick the first action\\n\",\n \" state = env.reset()\\n\",\n \" action_probs = policy(state)\\n\",\n \" action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\\n\",\n \" \\n\",\n \" # One step in the environment\\n\",\n \" for t in itertools.count():\\n\",\n \" # Take a step\\n\",\n \" next_state, reward, done, _ = env.step(action)\\n\",\n \" \\n\",\n \" # Pick the next action\\n\",\n \" next_action_probs = policy(next_state)\\n\",\n \" next_action = np.random.choice(np.arange(len(next_action_probs)), p=next_action_probs)\\n\",\n \" \\n\",\n \" # Update statistics\\n\",\n \" stats.episode_rewards[i_episode] += reward\\n\",\n \" stats.episode_lengths[i_episode] = t\\n\",\n \" \\n\",\n \" # TD Update\\n\",\n \" td_target = reward + discount_factor * Q[next_state][next_action]\\n\",\n \" td_delta = td_target - Q[state][action]\\n\",\n \" Q[state][action] += alpha * td_delta\\n\",\n \" \\n\",\n \" if done:\\n\",\n \" break\\n\",\n \" \\n\",\n \" action = next_action\\n\",\n \" state = next_state \\n\",\n \" \\n\",\n \" return Q, stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Episode 200/200.\"\n ]\n }\n ],\n \"source\": [\n \"Q, stats = sarsa(env, 200)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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FUVhw4dwqRJk5yey9kHcf78ede9OXIrZrMZOTk5Dd0MagR4rVBd8Hoh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\\nnd9+nA4myLyiA2rmQ+jPNpuu9N/Nw05ZBT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Ktzk4jI2LFjpV27djZtj6rG5mDStW/fHrt27UJCQoL++vzzz62Wadeu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\\n9Y3w8HA1Nja69mtsbOR4AwUHB7t+WaelpblmoyIiIrr9fqK/WE9XV5d27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5QbSx0RERE5PX3besjGtdBGTYfyu/wUavaApY6IiIiclug6ZM2nkI0/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\\nMCZyULHb/RYAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plotting.plot_episode_stats(stats)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "TD/SARSA.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import gym\\n\",\n \"import itertools\\n\",\n \"import matplotlib\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"\\n\",\n \"from collections import defaultdict\\n\",\n \"from lib.envs.windy_gridworld import WindyGridworldEnv\\n\",\n \"from lib import plotting\\n\",\n \"\\n\",\n \"matplotlib.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"env = WindyGridworldEnv()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def make_epsilon_greedy_policy(Q, epsilon, nA):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Creates an epsilon-greedy policy based on a given Q-function and epsilon.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" Q: A dictionary that maps from state -> action-values.\\n\",\n \" Each value is a numpy array of length nA (see below)\\n\",\n \" epsilon: The probability to select a random action . float between 0 and 1.\\n\",\n \" nA: Number of actions in the environment.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A function that takes the observation as an argument and returns\\n\",\n \" the probabilities for each action in the form of a numpy array of length nA.\\n\",\n \" \\n\",\n \" \\\"\\\"\\\"\\n\",\n \" def policy_fn(observation):\\n\",\n \" A = np.ones(nA, dtype=float) * epsilon / nA\\n\",\n \" best_action = np.argmax(Q[observation])\\n\",\n \" A[best_action] += (1.0 - epsilon)\\n\",\n \" return A\\n\",\n \" return policy_fn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def sarsa(env, num_episodes, discount_factor=1.0, alpha=0.5, epsilon=0.1):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" SARSA algorithm: On-policy TD control. Finds the optimal epsilon-greedy policy.\\n\",\n \" \\n\",\n \" Args:\\n\",\n \" env: OpenAI environment.\\n\",\n \" num_episodes: Number of episodes to run for.\\n\",\n \" discount_factor: Gamma discount factor.\\n\",\n \" alpha: TD learning rate.\\n\",\n \" epsilon: Chance the sample a random action. Float betwen 0 and 1.\\n\",\n \" \\n\",\n \" Returns:\\n\",\n \" A tuple (Q, stats).\\n\",\n \" Q is the optimal action-value function, a dictionary mapping state -> action values.\\n\",\n \" stats is an EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" \\n\",\n \" # The final action-value function.\\n\",\n \" # A nested dictionary that maps state -> (action -> action-value).\\n\",\n \" Q = defaultdict(lambda: np.zeros(env.action_space.n))\\n\",\n \" \\n\",\n \" # Keeps track of useful statistics\\n\",\n \" stats = plotting.EpisodeStats(\\n\",\n \" episode_lengths=np.zeros(num_episodes),\\n\",\n \" episode_rewards=np.zeros(num_episodes))\\n\",\n \"\\n\",\n \" # The policy we're following\\n\",\n \" policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\\n\",\n \" \\n\",\n \"\\n\",\n \" for i_episode in range(num_episodes):\\n\",\n \" # Print out which episode we're on, useful for debugging.\\n\",\n \" if (i_episode + 1) % 100 == 0:\\n\",\n \" print(\\\"\\\\rEpisode {}/{}.\\\".format(i_episode + 1, num_episodes), end=\\\"\\\")\\n\",\n \" sys.stdout.flush()\\n\",\n \" \\n\",\n \" # Implement this!\\n\",\n \" \\n\",\n \" return Q, stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Episode 200/200.\"\n ]\n }\n ],\n \"source\": [\n \"Q, stats = sarsa(env, 200)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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BUUFPgcJj07kj3wdegraAz6C/xFX0FjpKen+5xRSExM9BnD8+u0muDX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uO9vb1N9k0iImPHjpWOHTuqWh9Vjad6rVynTp2wd+9epKenK18LFy40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\\nePLkCQCgpaUFc+bMYTevAo3Pl7+Pw3r16h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I5ABwAAYDkCHQAAgOUIdAAAAJYj0AEAAFiOQAcAAGA5Ah0AAIDlCHQA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\\nAADLEegAAAAs938BJCULTMwc5aUAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plotting.plot_episode_stats(stats)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "TD/Windy Gridworld Playground.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import gym\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"if \\\"../\\\" not in sys.path:\\n\",\n \" sys.path.append(\\\"../\\\") \\n\",\n \"\\n\",\n \"from lib.envs.windy_gridworld import WindyGridworldEnv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"30\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"x o o o o o o T o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"\\n\",\n \"(31, -1.0, False, {'prob': 1.0})\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o x o o o o o T o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"\\n\",\n \"(32, -1.0, False, {'prob': 1.0})\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o x o o o o T o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"\\n\",\n \"(33, -1.0, False, {'prob': 1.0})\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o x o o o T o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"\\n\",\n \"(33, -1.0, False, {'prob': 1.0})\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o x o o o T o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"\\n\",\n \"(24, -1.0, False, {'prob': 1.0})\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o x o o o o o\\n\",\n \"o o o o o o o T o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"\\n\",\n \"(15, -1.0, False, {'prob': 1.0})\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o x o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o T o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"o o o o o o o o o o\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"env = WindyGridworldEnv()\\n\",\n \"\\n\",\n \"print(env.reset())\\n\",\n \"env.render()\\n\",\n \"\\n\",\n \"print(env.step(1))\\n\",\n \"env.render()\\n\",\n \"\\n\",\n \"print(env.step(1))\\n\",\n \"env.render()\\n\",\n \"\\n\",\n \"print(env.step(1))\\n\",\n \"env.render()\\n\",\n \"\\n\",\n \"print(env.step(2))\\n\",\n \"env.render()\\n\",\n \"\\n\",\n \"print(env.step(1))\\n\",\n \"env.render()\\n\",\n \"\\n\",\n \"print(env.step(1))\\n\",\n \"env.render()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "__init__.py", "content": "" }, { "path": "lib/__init__.py", "content": "" }, { "path": "lib/atari/__init__.py", "content": "\n" }, { "path": "lib/atari/helpers.py", "content": "import numpy as np\n\nclass AtariEnvWrapper(object):\n \"\"\"\n Wraps an Atari environment to end an episode when a life is lost.\n \"\"\"\n def __init__(self, env):\n self.env = env\n\n def __getattr__(self, name):\n return getattr(self.env, name)\n\n def step(self, *args, **kwargs):\n lives_before = self.env.ale.lives()\n next_state, reward, done, info = self.env.step(*args, **kwargs)\n lives_after = self.env.ale.lives()\n\n # End the episode when a life is lost\n if lives_before > lives_after:\n done = True\n\n # Clip rewards to [-1,1]\n reward = max(min(reward, 1), -1)\n\n return next_state, reward, done, info\n\ndef atari_make_initial_state(state):\n return np.stack([state] * 4, axis=2)\n\ndef atari_make_next_state(state, next_state):\n return np.append(state[:,:,1:], np.expand_dims(next_state, 2), axis=2)" }, { "path": "lib/atari/state_processor.py", "content": "import numpy as np\nimport tensorflow as tf\n\nclass StateProcessor():\n \"\"\"\n Processes a raw Atari iamges. Resizes it and converts it to grayscale.\n \"\"\"\n def __init__(self):\n # Build the Tensorflow graph\n with tf.variable_scope(\"state_processor\"):\n self.input_state = tf.placeholder(shape=[210, 160, 3], dtype=tf.uint8)\n self.output = tf.image.rgb_to_grayscale(self.input_state)\n self.output = tf.image.crop_to_bounding_box(self.output, 34, 0, 160, 160)\n self.output = tf.image.resize_images(\n self.output, [84, 84], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n self.output = tf.squeeze(self.output)\n\n def process(self, state, sess=None):\n \"\"\"\n Args:\n sess: A Tensorflow session object\n state: A [210, 160, 3] Atari RGB State\n\n Returns:\n A processed [84, 84, 1] state representing grayscale values.\n \"\"\"\n sess = sess or tf.get_default_session()\n return sess.run(self.output, { self.input_state: state })" }, { "path": "lib/envs/__init__.py", "content": "" }, { "path": "lib/envs/blackjack.py", "content": "import gym\nfrom gym import spaces\nfrom gym.utils import seeding\n\ndef cmp(a, b):\n return int((a > b)) - int((a < b))\n\n# 1 = Ace, 2-10 = Number cards, Jack/Queen/King = 10\ndeck = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\n\n\ndef draw_card(np_random):\n return np_random.choice(deck)\n\n\ndef draw_hand(np_random):\n return [draw_card(np_random), draw_card(np_random)]\n\n\ndef usable_ace(hand): # Does this hand have a usable ace?\n return 1 in hand and sum(hand) + 10 <= 21\n\n\ndef sum_hand(hand): # Return current hand total\n if usable_ace(hand):\n return sum(hand) + 10\n return sum(hand)\n\n\ndef is_bust(hand): # Is this hand a bust?\n return sum_hand(hand) > 21\n\n\ndef score(hand): # What is the score of this hand (0 if bust)\n return 0 if is_bust(hand) else sum_hand(hand)\n\n\ndef is_natural(hand): # Is this hand a natural blackjack?\n return sorted(hand) == [1, 10]\n\n\nclass BlackjackEnv(gym.Env):\n \"\"\"Simple blackjack environment\n Blackjack is a card game where the goal is to obtain cards that sum to as\n near as possible to 21 without going over. They're playing against a fixed\n dealer.\n Face cards (Jack, Queen, King) have point value 10.\n Aces can either count as 11 or 1, and it's called 'usable' at 11.\n This game is placed with an infinite deck (or with replacement).\n The game starts with each (player and dealer) having one face up and one\n face down card.\n The player can request additional cards (hit=1) until they decide to stop\n (stick=0) or exceed 21 (bust).\n After the player sticks, the dealer reveals their facedown card, and draws\n until their sum is 17 or greater. If the dealer goes bust the player wins.\n If neither player nor dealer busts, the outcome (win, lose, draw) is\n decided by whose sum is closer to 21. The reward for winning is +1,\n drawing is 0, and losing is -1.\n The observation of a 3-tuple of: the players current sum,\n the dealer's one showing card (1-10 where 1 is ace),\n and whether or not the player holds a usable ace (0 or 1).\n This environment corresponds to the version of the blackjack problem\n described in Example 5.1 in Reinforcement Learning: An Introduction\n by Sutton and Barto (1998).\n https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html\n \"\"\"\n def __init__(self, natural=False):\n self.action_space = spaces.Discrete(2)\n self.observation_space = spaces.Tuple((\n spaces.Discrete(32),\n spaces.Discrete(11),\n spaces.Discrete(2)))\n self._seed()\n\n # Flag to payout 1.5 on a \"natural\" blackjack win, like casino rules\n # Ref: http://www.bicyclecards.com/how-to-play/blackjack/\n self.natural = natural\n # Start the first game\n self._reset() # Number of \n self.nA = 2\n\n def reset(self):\n return self._reset()\n\n def step(self, action):\n return self._step(action)\n\n def _seed(self, seed=None):\n self.np_random, seed = seeding.np_random(seed)\n return [seed]\n\n def _step(self, action):\n assert self.action_space.contains(action)\n if action: # hit: add a card to players hand and return\n self.player.append(draw_card(self.np_random))\n if is_bust(self.player):\n done = True\n reward = -1\n else:\n done = False\n reward = 0\n else: # stick: play out the dealers hand, and score\n done = True\n while sum_hand(self.dealer) < 17:\n self.dealer.append(draw_card(self.np_random))\n reward = cmp(score(self.player), score(self.dealer))\n if self.natural and is_natural(self.player) and reward == 1:\n reward = 1.5\n return self._get_obs(), reward, done, {}\n\n def _get_obs(self):\n return (sum_hand(self.player), self.dealer[0], usable_ace(self.player))\n\n def _reset(self):\n self.dealer = draw_hand(self.np_random)\n self.player = draw_hand(self.np_random)\n\n # Auto-draw another card if the score is less than 12\n while sum_hand(self.player) < 12:\n self.player.append(draw_card(self.np_random))\n\n return self._get_obs()\n" }, { "path": "lib/envs/cliff_walking.py", "content": "import io\nimport numpy as np\nimport sys\n\nfrom . import discrete\n\nUP = 0\nRIGHT = 1\nDOWN = 2\nLEFT = 3\n\nclass CliffWalkingEnv(discrete.DiscreteEnv):\n\n metadata = {'render.modes': ['human', 'ansi']}\n\n def _limit_coordinates(self, coord):\n coord[0] = min(coord[0], self.shape[0] - 1)\n coord[0] = max(coord[0], 0)\n coord[1] = min(coord[1], self.shape[1] - 1)\n coord[1] = max(coord[1], 0)\n return coord\n\n def _calculate_transition_prob(self, current, delta):\n new_position = np.array(current) + np.array(delta)\n new_position = self._limit_coordinates(new_position).astype(int)\n new_state = np.ravel_multi_index(tuple(new_position), self.shape)\n reward = -100.0 if self._cliff[tuple(new_position)] else -1.0\n is_done = self._cliff[tuple(new_position)] or (tuple(new_position) == (3,11))\n return [(1.0, new_state, reward, is_done)]\n\n def __init__(self):\n self.shape = (4, 12)\n\n nS = np.prod(self.shape)\n nA = 4\n\n # Cliff Location\n self._cliff = np.zeros(self.shape, dtype=np.bool)\n self._cliff[3, 1:-1] = True\n\n # Calculate transition probabilities\n P = {}\n for s in range(nS):\n position = np.unravel_index(s, self.shape)\n P[s] = { a : [] for a in range(nA) }\n P[s][UP] = self._calculate_transition_prob(position, [-1, 0])\n P[s][RIGHT] = self._calculate_transition_prob(position, [0, 1])\n P[s][DOWN] = self._calculate_transition_prob(position, [1, 0])\n P[s][LEFT] = self._calculate_transition_prob(position, [0, -1])\n\n # We always start in state (3, 0)\n isd = np.zeros(nS)\n isd[np.ravel_multi_index((3,0), self.shape)] = 1.0\n\n super(CliffWalkingEnv, self).__init__(nS, nA, P, isd)\n\n def render(self, mode='human', close=False):\n self._render(mode, close)\n\n def _render(self, mode='human', close=False):\n if close:\n return\n\n outfile = io.StringIO() if mode == 'ansi' else sys.stdout\n\n for s in range(self.nS):\n position = np.unravel_index(s, self.shape)\n # print(self.s)\n if self.s == s:\n output = \" x \"\n elif position == (3,11):\n output = \" T \"\n elif self._cliff[position]:\n output = \" C \"\n else:\n output = \" o \"\n\n if position[1] == 0:\n output = output.lstrip() \n if position[1] == self.shape[1] - 1:\n output = output.rstrip() \n output += \"\\n\"\n\n outfile.write(output)\n outfile.write(\"\\n\")\n" }, { "path": "lib/envs/discrete.py", "content": "import numpy as np\n\nfrom gym import Env, spaces\nfrom gym.utils import seeding\nfrom gym.envs.toy_text.utils import categorical_sample\n\nclass DiscreteEnv(Env):\n\n \"\"\"\n Has the following members\n - nS: number of states\n - nA: number of actions\n - P: transitions (*)\n - isd: initial state distribution (**)\n\n (*) dictionary of lists, where\n P[s][a] == [(probability, nextstate, reward, done), ...]\n (**) list or array of length nS\n\n\n \"\"\"\n\n def __init__(self, nS, nA, P, isd):\n self.P = P\n self.isd = isd\n self.lastaction = None # for rendering\n self.nS = nS\n self.nA = nA\n\n self.action_space = spaces.Discrete(self.nA)\n self.observation_space = spaces.Discrete(self.nS)\n\n self.seed()\n self.s = categorical_sample(self.isd, self.np_random)\n\n def seed(self, seed=None):\n self.np_random, seed = seeding.np_random(seed)\n return [seed]\n\n def reset(self):\n self.s = categorical_sample(self.isd, self.np_random)\n self.lastaction = None\n return int(self.s)\n\n def step(self, a):\n transitions = self.P[self.s][a]\n i = categorical_sample([t[0] for t in transitions], self.np_random)\n p, s, r, d = transitions[i]\n self.s = s\n self.lastaction = a\n return (int(s), r, d, {\"prob\": p})\n" }, { "path": "lib/envs/gridworld.py", "content": "import io\nimport numpy as np\nimport sys\n\nfrom . import discrete\n\nUP = 0\nRIGHT = 1\nDOWN = 2\nLEFT = 3\n\nclass GridworldEnv(discrete.DiscreteEnv):\n \"\"\"\n Grid World environment from Sutton's Reinforcement Learning book chapter 4.\n You are an agent on an MxN grid and your goal is to reach the terminal\n state at the top left or the bottom right corner.\n\n For example, a 4x4 grid looks as follows:\n\n T o o o\n o x o o\n o o o o\n o o o T\n\n x is your position and T are the two terminal states.\n\n You can take actions in each direction (UP=0, RIGHT=1, DOWN=2, LEFT=3).\n Actions going off the edge leave you in your current state.\n You receive a reward of -1 at each step until you reach a terminal state.\n \"\"\"\n\n metadata = {'render.modes': ['human', 'ansi']}\n\n def __init__(self, shape=[4,4]):\n if not isinstance(shape, (list, tuple)) or not len(shape) == 2:\n raise ValueError('shape argument must be a list/tuple of length 2')\n\n self.shape = shape\n\n nS = np.prod(shape)\n nA = 4\n\n MAX_Y = shape[0]\n MAX_X = shape[1]\n\n P = {}\n grid = np.arange(nS).reshape(shape)\n it = np.nditer(grid, flags=['multi_index'])\n\n while not it.finished:\n s = it.iterindex\n y, x = it.multi_index\n\n # P[s][a] = (prob, next_state, reward, is_done)\n P[s] = {a : [] for a in range(nA)}\n\n is_done = lambda s: s == 0 or s == (nS - 1)\n reward = 0.0 if is_done(s) else -1.0\n\n # We're stuck in a terminal state\n if is_done(s):\n P[s][UP] = [(1.0, s, reward, True)]\n P[s][RIGHT] = [(1.0, s, reward, True)]\n P[s][DOWN] = [(1.0, s, reward, True)]\n P[s][LEFT] = [(1.0, s, reward, True)]\n # Not a terminal state\n else:\n ns_up = s if y == 0 else s - MAX_X\n ns_right = s if x == (MAX_X - 1) else s + 1\n ns_down = s if y == (MAX_Y - 1) else s + MAX_X\n ns_left = s if x == 0 else s - 1\n P[s][UP] = [(1.0, ns_up, reward, is_done(ns_up))]\n P[s][RIGHT] = [(1.0, ns_right, reward, is_done(ns_right))]\n P[s][DOWN] = [(1.0, ns_down, reward, is_done(ns_down))]\n P[s][LEFT] = [(1.0, ns_left, reward, is_done(ns_left))]\n\n it.iternext()\n\n # Initial state distribution is uniform\n isd = np.ones(nS) / nS\n\n # We expose the model of the environment for educational purposes\n # This should not be used in any model-free learning algorithm\n self.P = P\n\n super(GridworldEnv, self).__init__(nS, nA, P, isd)\n\n def _render(self, mode='human', close=False):\n \"\"\" Renders the current gridworld layout\n\n For example, a 4x4 grid with the mode=\"human\" looks like:\n T o o o\n o x o o\n o o o o\n o o o T\n where x is your position and T are the two terminal states.\n \"\"\"\n if close:\n return\n\n outfile = io.StringIO() if mode == 'ansi' else sys.stdout\n\n grid = np.arange(self.nS).reshape(self.shape)\n it = np.nditer(grid, flags=['multi_index'])\n while not it.finished:\n s = it.iterindex\n y, x = it.multi_index\n\n if self.s == s:\n output = \" x \"\n elif s == 0 or s == self.nS - 1:\n output = \" T \"\n else:\n output = \" o \"\n\n if x == 0:\n output = output.lstrip()\n if x == self.shape[1] - 1:\n output = output.rstrip()\n\n outfile.write(output)\n\n if x == self.shape[1] - 1:\n outfile.write(\"\\n\")\n\n it.iternext()\n" }, { "path": "lib/envs/windy_gridworld.py", "content": "import io\nimport gym\nimport numpy as np\nimport sys\n\nfrom . import discrete\n\nUP = 0\nRIGHT = 1\nDOWN = 2\nLEFT = 3\n\nclass WindyGridworldEnv(discrete.DiscreteEnv):\n\n metadata = {'render.modes': ['human', 'ansi']}\n\n def _limit_coordinates(self, coord):\n coord[0] = min(coord[0], self.shape[0] - 1)\n coord[0] = max(coord[0], 0)\n coord[1] = min(coord[1], self.shape[1] - 1)\n coord[1] = max(coord[1], 0)\n return coord\n\n def _calculate_transition_prob(self, current, delta, winds):\n new_position = np.array(current) + np.array(delta) + np.array([-1, 0]) * winds[tuple(current)]\n new_position = self._limit_coordinates(new_position).astype(int)\n new_state = np.ravel_multi_index(tuple(new_position), self.shape)\n is_done = tuple(new_position) == (3, 7)\n return [(1.0, new_state, -1.0, is_done)]\n\n def __init__(self):\n self.shape = (7, 10)\n\n nS = np.prod(self.shape)\n nA = 4\n\n # Wind strength\n winds = np.zeros(self.shape)\n winds[:,[3,4,5,8]] = 1\n winds[:,[6,7]] = 2\n\n # Calculate transition probabilities\n P = {}\n for s in range(nS):\n position = np.unravel_index(s, self.shape)\n P[s] = { a : [] for a in range(nA) }\n P[s][UP] = self._calculate_transition_prob(position, [-1, 0], winds)\n P[s][RIGHT] = self._calculate_transition_prob(position, [0, 1], winds)\n P[s][DOWN] = self._calculate_transition_prob(position, [1, 0], winds)\n P[s][LEFT] = self._calculate_transition_prob(position, [0, -1], winds)\n\n # We always start in state (3, 0)\n isd = np.zeros(nS)\n isd[np.ravel_multi_index((3,0), self.shape)] = 1.0\n\n super(WindyGridworldEnv, self).__init__(nS, nA, P, isd)\n\n def render(self, mode='human', close=False):\n self._render(mode, close)\n\n def _render(self, mode='human', close=False):\n if close:\n return\n\n outfile = io.StringIO() if mode == 'ansi' else sys.stdout\n\n for s in range(self.nS):\n position = np.unravel_index(s, self.shape)\n # print(self.s)\n if self.s == s:\n output = \" x \"\n elif position == (3,7):\n output = \" T \"\n else:\n output = \" o \"\n\n if position[1] == 0:\n output = output.lstrip()\n if position[1] == self.shape[1] - 1:\n output = output.rstrip()\n output += \"\\n\"\n\n outfile.write(output)\n outfile.write(\"\\n\")\n" }, { "path": "lib/plotting.py", "content": "import matplotlib\nimport numpy as np\nimport pandas as pd\nfrom collections import namedtuple\nfrom matplotlib import pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nEpisodeStats = namedtuple(\"Stats\",[\"episode_lengths\", \"episode_rewards\"])\n\ndef plot_cost_to_go_mountain_car(env, estimator, num_tiles=20):\n x = np.linspace(env.observation_space.low[0], env.observation_space.high[0], num=num_tiles)\n y = np.linspace(env.observation_space.low[1], env.observation_space.high[1], num=num_tiles)\n X, Y = np.meshgrid(x, y)\n Z = np.apply_along_axis(lambda _: -np.max(estimator.predict(_)), 2, np.dstack([X, Y]))\n\n fig = plt.figure(figsize=(10, 5))\n ax = fig.add_subplot(111, projection='3d')\n surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\n cmap=matplotlib.cm.coolwarm, vmin=-1.0, vmax=1.0)\n ax.set_xlabel('Position')\n ax.set_ylabel('Velocity')\n ax.set_zlabel('Value')\n ax.set_title(\"Mountain \\\"Cost To Go\\\" Function\")\n fig.colorbar(surf)\n plt.show()\n\n\ndef plot_value_function(V, title=\"Value Function\"):\n \"\"\"\n Plots the value function as a surface plot.\n \"\"\"\n min_x = min(k[0] for k in V.keys())\n max_x = max(k[0] for k in V.keys())\n min_y = min(k[1] for k in V.keys())\n max_y = max(k[1] for k in V.keys())\n\n x_range = np.arange(min_x, max_x + 1)\n y_range = np.arange(min_y, max_y + 1)\n X, Y = np.meshgrid(x_range, y_range)\n\n # Find value for all (x, y) coordinates\n Z_noace = np.apply_along_axis(lambda _: V[(_[0], _[1], False)], 2, np.dstack([X, Y]))\n Z_ace = np.apply_along_axis(lambda _: V[(_[0], _[1], True)], 2, np.dstack([X, Y]))\n\n def plot_surface(X, Y, Z, title):\n fig = plt.figure(figsize=(20, 10))\n ax = fig.add_subplot(111, projection='3d')\n surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\n cmap=matplotlib.cm.coolwarm, vmin=-1.0, vmax=1.0)\n ax.set_xlabel('Player Sum')\n ax.set_ylabel('Dealer Showing')\n ax.set_zlabel('Value')\n ax.set_title(title)\n ax.view_init(ax.elev, -120)\n fig.colorbar(surf)\n plt.show()\n\n plot_surface(X, Y, Z_noace, \"{} (No Usable Ace)\".format(title))\n plot_surface(X, Y, Z_ace, \"{} (Usable Ace)\".format(title))\n\n\n\ndef plot_episode_stats(stats, smoothing_window=10, noshow=False):\n # Plot the episode length over time\n fig1 = plt.figure(figsize=(10,5))\n plt.plot(stats.episode_lengths)\n plt.xlabel(\"Episode\")\n plt.ylabel(\"Episode Length\")\n plt.title(\"Episode Length over Time\")\n if noshow:\n plt.close(fig1)\n else:\n plt.show(fig1)\n\n # Plot the episode reward over time\n fig2 = plt.figure(figsize=(10,5))\n rewards_smoothed = pd.Series(stats.episode_rewards).rolling(smoothing_window, min_periods=smoothing_window).mean()\n plt.plot(rewards_smoothed)\n plt.xlabel(\"Episode\")\n plt.ylabel(\"Episode Reward (Smoothed)\")\n plt.title(\"Episode Reward over Time (Smoothed over window size {})\".format(smoothing_window))\n if noshow:\n plt.close(fig2)\n else:\n plt.show(fig2)\n\n # Plot time steps and episode number\n fig3 = plt.figure(figsize=(10,5))\n plt.plot(np.cumsum(stats.episode_lengths), np.arange(len(stats.episode_lengths)))\n plt.xlabel(\"Time Steps\")\n plt.ylabel(\"Episode\")\n plt.title(\"Episode per time step\")\n if noshow:\n plt.close(fig3)\n else:\n plt.show(fig3)\n\n return fig1, fig2, fig3\n" } ]