Repository: xiaochus/Deep-Reinforcement-Learning-Practice
Branch: master
Commit: 6b2efff2d605
Files: 35
Total size: 226.9 KB

Directory structure:
gitextract_0r3h2dqf/

├── .gitignore
├── A3C_sparse.py
├── AC_continue.py
├── AC_sparse.py
├── DDPG.py
├── DQN.py
├── DRL.py
├── DoubleDQN.py
├── DuelingDQN.py
├── NatureDQN.py
├── PPO_TF.py
├── PolicyNetwork.py
├── README.md
├── game/
│   ├── CartPole.py
│   └── Pendulum.py
├── history/
│   ├── a3c_sparse.csv
│   ├── ac_continue.csv
│   ├── ac_sparse.csv
│   ├── ddpg.csv
│   ├── ddqn.csv
│   ├── dueling.csv
│   ├── ndqn.csv
│   ├── pg.csv
│   ├── ppo1.csv
│   └── ppo2.csv
└── model/
    ├── actor_a3cs.h5
    ├── actor_acs.h5
    ├── critic_a3cs.h5
    ├── critic_acs.h5
    ├── ddpg_actor.h5
    ├── ddpg_critic.h5
    ├── ddqn.h5
    ├── dueling.h5
    ├── ndqn.h5
    └── pg.h5

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
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================================================
FILE: A3C_sparse.py
================================================
# -*- coding: utf-8 -*-
import os
import gym
import time
import threading

import numpy as np
import pandas as pd
import tensorflow as tf

from keras.layers import Input, Dense
from keras.models import Model
from keras.optimizers import Adam
import keras.backend as K


# global variables for threading
step = 0
history = {'episode': [], 'Episode_reward': []}
lock = threading.Lock()


class A3C:
    """A3C Algorithms with sparse action.
    """
    def __init__(self):
        self.gamma = 0.95
        self.actor_lr = 0.001
        self.critic_lr = 0.01

        self._build_model()
        self.optimizer = self._build_optimizer()

        # handle error
        self.sess = tf.InteractiveSession()
        K.set_session(self.sess)
        self.sess.run(tf.global_variables_initializer())

    def _build_actor(self):
        """actor model.
        """
        inputs = Input(shape=(4,))
        x = Dense(20, activation='relu')(inputs)
        x = Dense(20, activation='relu')(x)
        x = Dense(1, activation='sigmoid')(x)

        model = Model(inputs=inputs, outputs=x)

        return model

    def _build_critic(self):
        """critic model.
        """
        inputs = Input(shape=(4,))
        x = Dense(20, activation='relu')(inputs)
        x = Dense(20, activation='relu')(x)
        x = Dense(1, activation='linear')(x)

        model = Model(inputs=inputs, outputs=x)

        return model

    def _build_model(self):
        """build model for multi threading training.
        """
        self.actor = self._build_actor()
        self.critic = self._build_critic()

        # Pre-compile for threading
        self.actor._make_predict_function()
        self.critic._make_predict_function()

    def _build_optimizer(self):
        """build optimizer and loss method.

        Returns:
            [actor optimizer, critic optimizer].
        """
        # actor optimizer
        actions = K.placeholder(shape=(None, 1))
        advantages = K.placeholder(shape=(None, 1))
        action_pred = self.actor.output

        entropy = K.sum(action_pred * K.log(action_pred + 1e-10), axis=1)
        closs = K.binary_crossentropy(actions, action_pred)
        actor_loss = K.mean(closs * K.flatten(advantages)) - 0.01 * entropy

        actor_optimizer = Adam(lr=self.actor_lr)
        actor_updates = actor_optimizer.get_updates(self.actor.trainable_weights, [], actor_loss)
        actor_train = K.function([self.actor.input, actions, advantages], [], updates=actor_updates)

        # critic optimizer
        discounted_reward = K.placeholder(shape=(None, 1))
        value = self.critic.output

        critic_loss = K.mean(K.square(discounted_reward - value))

        critic_optimizer = Adam(lr=self.critic_lr)
        critic_updates = critic_optimizer.get_updates(self.critic.trainable_weights, [], critic_loss)
        critic_train = K.function([self.critic.input, discounted_reward], [], updates=critic_updates)

        return [actor_train, critic_train]

    def train(self, episode, n_thread, update_iter):
        """training A3C.

        Arguments:
            episode: total training episode.
            n_thread: number of thread.
            update_iter: update iter.
        """
        # Multi threading training.
        threads = [Agent(i, self.actor, self.critic, self.optimizer, self.gamma, episode, update_iter) for i in range(n_thread)]

        for t in threads:
            t.start()
            time.sleep(1)

        try:
            [t.join() for t in threads]
        except KeyboardInterrupt:
            print("Exiting all threads...")

        self.save()

    def load(self):
        """Load model weights.
        """
        if os.path.exists('model/actor_a3cs.h5') and os.path.exists('model/critic_a3cs.h5'):
            self.actor.load_weights('model/actor_a3cs.h5')
            self.critic.load_weights('model/critic_a3cs.h5')

    def save(self):
        """Save model weights.
        """
        self.actor.save_weights('model/actor_a3cs.h5')
        self.critic.save_weights('model/critic_a3cs.h5')


class Agent(threading.Thread):
    """Multi threading training agent.
    """
    def __init__(self, index, actor, critic, optimizer, gamma, episode, update_iter):
        threading.Thread.__init__(self)

        self.index = index
        self.actor = actor
        self.critic = critic
        self.optimizer = optimizer
        self.gamma = gamma
        self.episode = episode
        self.update_iter = update_iter

        self.env = gym.make('CartPole-v0')

    def run(self):
        """training model.
        """
        global history
        global step

        while step < self.episode:
            observation = self.env.reset()

            states = []
            actions = []
            rewards = []

            while True:
                x = observation.reshape(-1, 4)
                states.append(x)

                # choice action with prob.
                prob = self.actor.predict(x)[0][0]
                action = np.random.choice(np.array(range(2)), p=[1 - prob, prob])
                actions.append(action)

                next_observation, reward, done, _ = self.env.step(action)
                next_observation = next_observation.reshape(-1, 4)
                rewards.append(reward)

                observation = next_observation[0]

                if ((step + 1) % self.update_iter == 0) or done:
                    lock.acquire()
                    try:
                        self.train_episode(states, actions, rewards, next_observation, done)

                        if done:
                            episode_reward = sum(rewards)
                            history['episode'].append(step)
                            history['Episode_reward'].append(episode_reward)

                            print('Thread: {} | Episode: {} | Episode reward: {}'.format(self.index, step, episode_reward))

                            step += 1
                    finally:
                        lock.release()

                if done:
                    break

    def discount_reward(self, rewards, next_state, done):
        """Discount reward

        Arguments:
            rewards: rewards in a episode.
            next_states: next state of current game step.
            done: if epsiode done.

        Returns:
            discount_reward: n-step discount rewards.
        """
        # compute the discounted reward backwards through time.
        discount_rewards = np.zeros_like(rewards, dtype=np.float32)

        if done:
            cumulative = 0.
        else:            
            cumulative = self.critic.predict(next_state)[0][0]

        for i in reversed(range(len(rewards))):
            cumulative = cumulative * self.gamma + rewards[i]
            discount_rewards[i] = cumulative

        return discount_rewards

    def train_episode(self, states, actions, rewards, next_observation, done):
        """training algorithm in an epsiode.
        """
        states = np.concatenate(states, axis=0)
        actions = np.array(actions).reshape(-1, 1)
        rewards = np.array(rewards)

        # Q_values
        values = self.critic.predict(states)
        # discounted rewards
        discounted_rewards = self.discount_reward(rewards, next_observation, done)
        discounted_rewards = discounted_rewards.reshape(-1, 1)
        # advantages
        advantages = discounted_rewards - values

        self.optimizer[1]([states, discounted_rewards])
        self.optimizer[0]([states, actions, advantages])


def save_history(history, name):
    """save reward history.
    """
    name = os.path.join('history', name)

    df = pd.DataFrame.from_dict(history)
    df.to_csv(name, index=False, encoding='utf-8')


def play(model):
    """play game with model.
    """
    print('play...')

    env = gym.make('CartPole-v0')
    observation = env.reset()

    reward_sum = 0
    random_episodes = 0

    while random_episodes < 10:
        env.render()

        prob = model.actor.predict(observation.reshape(-1, 4))[0][0]
        action = 1 if prob > 0.5 else 0

        observation, reward, done, _ = env.step(action)

        reward_sum += reward

        if done:
            print("Reward for this episode was: {}".format(reward_sum))
            random_episodes += 1
            reward_sum = 0
            observation = env.reset()

    env.close()


if __name__ == '__main__':
    model = A3C()
#
#    model.train(2000, 4, 10)
#    save_history(history, 'a3c_sparse.csv')

    model.load()
    play(model)


================================================
FILE: AC_continue.py
================================================
# -*- coding: utf-8 -*-
import os
import gym
import numpy as np

from keras.layers import Input, Dense, concatenate, Lambda
from keras.models import Model
from keras.optimizers import Adam
import keras.backend as K

from DRL import DRL


class AC(DRL):
    """Actor Critic Algorithms with continuous action.
       not stable during training.
    """
    def __init__(self):
        super(AC, self).__init__()

        self.env = gym.make('Pendulum-v0')
        self.bound = self.env.action_space.high[0]

        self.actor = self._build_actor()
        self.critic = self._build_critic()

        if os.path.exists('model/actor_acc.h5') and os.path.exists('model/critic_acc.h5'):
            self.actor.load_weights('model/actor_acc.h5')
            self.critic.load_weights('model/critic_acc.h5')

        self.gamma = 0.9

    def _build_actor(self):
        """actor model.
        """
        inputs = Input(shape=(3,))
        x = Dense(20, activation='relu')(inputs)
        x = Dense(20, activation='relu')(x)

        mu = Dense(1, activation='tanh')(x)
        mu = Lambda(lambda x: self.bound * x)(mu)

        sigma = Dense(1, activation='softplus')(x)
        sigma = Lambda(lambda x: x + 0.0001)(sigma)

        out = concatenate([mu, sigma], axis=-1)

        model = Model(inputs=inputs, outputs=out)

        return model

    def _build_critic(self):
        """critic model.
        """
        inputs = Input(shape=(3,))
        x = Dense(20, activation='relu')(inputs)
        x = Dense(20, activation='relu')(x)
        x = Dense(1, activation='linear')(x)

        model = Model(inputs=inputs, outputs=x)

        return model

    def _actor_loss(self, y_true, y_pred):
        """actor loss function.

        Arguments:
            y_true: (action, reward)
            y_pred: action

        Returns:
            loss: reward loss
        """
        mu, sigma = y_pred[:, 0], y_pred[:, 1]
        action_true, td_error = y_true[:, 0], y_true[:, 1]

        # probability density function
        pdf = 1. / K.sqrt(2. * np.pi * sigma) * K.exp(-K.square(action_true - mu) / (2. * sigma))
        log_pdf = K.log(pdf + K.epsilon())
        # entropy for explore
        entropy = K.sum(0.5 * (K.log(2. * np.pi * sigma) + 1.))

        exp_v = log_pdf * td_error

        exp_v = K.sum(exp_v + 0.01 * entropy)
        loss = -exp_v

        return loss

    def discount_reward(self, next_states, reward):
        """Discount reward for critic

        Arguments:
            next_states: next_states
            rewards: reward of last action.
            done: if game done.
        """
        q = self.critic.predict(next_states)[0][0]
        target = reward + self.gamma * q

        return target

    def choice_action(self, x):
        """choice continuous action from normal distributions.

        Arguments:
            x: state

        Returns:
            action: action
        """
        mu, sigma = self.actor.predict(x)[0]
        epsilon = np.random.randn(1)[0]
        action = mu + np.sqrt(sigma) * epsilon

        action = np.clip(action, -self.bound, self.bound)

        return action

    def train(self, episode):
        """training model.

        Arguments:
            episode: ganme episode

        Returns:
            history: training history
        """
        self.actor.compile(loss=self._actor_loss, optimizer=Adam(lr=0.001))
        self.critic.compile(loss='mse', optimizer=Adam(lr=0.002))

        history = {'episode': [], 'Episode_reward': [],
                   'actor_loss': [], 'critic_loss': []}

        for i in range(episode):
            observation = self.env.reset()
            rewards = []
            alosses = []
            closses = []

            while True:
                x = observation.reshape(-1, 3)

                action = self.choice_action(x)
                next_observation, reward, done, _ = self.env.step([action])
                next_observation = next_observation.reshape(-1, 3)

                rewards.append(reward)

                target = self.discount_reward(next_observation, reward)
                y = np.array([target])
                # TD_error = (r + gamma * next_q) - current_q
                td_error = target - self.critic.predict(x)[0][0]
                # loss1 = mse((r + gamma * next_q), current_q)
                loss1 = self.critic.train_on_batch(x, y)

                y = np.array([[action, td_error]])
                loss2 = self.actor.train_on_batch(x, y)

                observation = next_observation[0]

                alosses.append(loss2)
                closses.append(loss1)

                if done:
                    episode_reward = np.sum(rewards)
                    aloss = np.mean(alosses)
                    closs = np.mean(closses)

                    history['episode'].append(i)
                    history['Episode_reward'].append(episode_reward)
                    history['actor_loss'].append(aloss)
                    history['critic_loss'].append(closs)

                    print('Episode: {} | Episode reward: {:.2f} | actor_loss: {:.3f} | critic_loss: {:.3f}'.format(i, episode_reward, aloss, closs))

                    break

        self.actor.save_weights('model/actor_acc.h5')
        self.critic.save_weights('model/critic_acc.h5')

        return history

    def play(self):
        """play game with model.
        """
        print('play...')
        observation = self.env.reset()

        reward_sum = 0
        random_episodes = 0

        while random_episodes < 10:
            self.env.render()

            x = observation.reshape(-1, 3)
            action = self.choice_action(x)
            observation, reward, done, _ = self.env.step([action])
 
            reward_sum += reward

            if done:
                print("Reward for this episode was: {}".format(reward_sum))
                random_episodes += 1
                reward_sum = 0
                observation = self.env.reset()

        self.env.close()


if __name__ == '__main__':
    model = AC()

    history = model.train(500)
    model.save_history(history, 'ac_continue.csv')

    model.play()


================================================
FILE: AC_sparse.py
================================================
# -*- coding: utf-8 -*-
import os

import numpy as np

from keras.layers import Input, Dense
from keras.models import Model
from keras.optimizers import Adam
import keras.backend as K

from DRL import DRL


class AC(DRL):
    """Actor Critic Algorithms with sparse action.
    """
    def __init__(self):
        super(AC, self).__init__()

        self.actor = self._build_actor()
        self.critic = self._build_critic()

        self.gamma = 0.9

    def load(self):
        if os.path.exists('model/actor_acs.h5') and os.path.exists('model/critic_acs.h5'):
            self.actor.load_weights('model/actor_acs.h5')
            self.critic.load_weights('model/critic_acs.h5')

    def _build_actor(self):
        """actor model.
        """
        inputs = Input(shape=(4,))
        x = Dense(20, activation='relu')(inputs)
        x = Dense(20, activation='relu')(x)
        x = Dense(1, activation='sigmoid')(x)

        model = Model(inputs=inputs, outputs=x)

        return model

    def _build_critic(self):
        """critic model.
        """
        inputs = Input(shape=(4,))
        x = Dense(20, activation='relu')(inputs)
        x = Dense(20, activation='relu')(x)
        x = Dense(1, activation='linear')(x)

        model = Model(inputs=inputs, outputs=x)

        return model

    def _actor_loss(self, y_true, y_pred):
        """actor loss function.

        Arguments:
            y_true: (action, reward)
            y_pred: action_prob

        Returns:
            loss: reward loss
        """
        action_pred = y_pred
        action_true, td_error = y_true[:, 0], y_true[:, 1]
        action_true = K.reshape(action_true, (-1, 1))

        loss = K.binary_crossentropy(action_true, action_pred)
        loss = loss * K.flatten(td_error)

        return loss

    def discount_reward(self, next_states, reward, done):
        """Discount reward for Critic

        Arguments:
            next_states: next_states
            rewards: reward of last action.
            done: if game done.
        """
        q = self.critic.predict(next_states)[0][0]

        target = reward
        if not done:
            target = reward + self.gamma * q

        return target

    def train(self, episode):
        """training model.

        Arguments:
            episode: ganme episode

        Returns:
            history: training history
        """
        self.actor.compile(loss=self._actor_loss, optimizer=Adam(lr=0.001))
        self.critic.compile(loss='mse', optimizer=Adam(lr=0.01))

        history = {'episode': [], 'Episode_reward': [],
                   'actor_loss': [], 'critic_loss': []}

        for i in range(episode):
            observation = self.env.reset()
            rewards = []
            alosses = []
            closses = []

            while True:
                x = observation.reshape(-1, 4)
                # choice action with prob.
                prob = self.actor.predict(x)[0][0]
                action = np.random.choice(np.array(range(2)), p=[1 - prob, prob])

                next_observation, reward, done, _ = self.env.step(action)
                next_observation = next_observation.reshape(-1, 4)
                rewards.append(reward)

                target = self.discount_reward(next_observation, reward, done)
                y = np.array([target])

                # TD_error = (r + gamma * next_q) - current_q
                td_error = target - self.critic.predict(x)[0][0]
                # loss1 = mse((r + gamma * next_q), current_q)
                loss1 = self.critic.train_on_batch(x, y)

                y = np.array([[action, td_error]])
                loss2 = self.actor.train_on_batch(x, y)

                observation = next_observation[0]

                alosses.append(loss2)
                closses.append(loss1)

                if done:
                    episode_reward = sum(rewards)
                    aloss = np.mean(alosses)
                    closs = np.mean(closses)

                    history['episode'].append(i)
                    history['Episode_reward'].append(episode_reward)
                    history['actor_loss'].append(aloss)
                    history['critic_loss'].append(closs)

                    print('Episode: {} | Episode reward: {} | actor_loss: {:.3f} | critic_loss: {:.3f}'.format(i, episode_reward, aloss, closs))

                    break

        self.actor.save_weights('model/actor_acs.h5')
        self.critic.save_weights('model/critic_acs.h5')

        return history


if __name__ == '__main__':
    model = AC()

    history = model.train(300)
    model.save_history(history, 'ac_sparse.csv')
    
    model.load()
    model.play('acs')


================================================
FILE: DDPG.py
================================================
# -*- coding: utf-8 -*-
import os
import random
import gym
from collections import deque

import numpy as np
import tensorflow as tf

from keras.layers import Input, Dense, Lambda, concatenate
from keras.models import Model
from keras.optimizers import Adam
import keras.backend as K

from DRL import DRL


class DDPG(DRL):
    """Deep Deterministic Policy Gradient Algorithms.
    """
    def __init__(self):
        super(DDPG, self).__init__()

        self.sess = K.get_session()
        self.env = gym.make('Pendulum-v0')
        self.bound = self.env.action_space.high[0]

        # update rate for target model.
        self.TAU = 0.01
        # experience replay.
        self.memory_buffer = deque(maxlen=4000)
        # discount rate for q value.
        self.gamma = 0.95
        # epsilon of action selection
        self.epsilon = 1.0
        # discount rate for epsilon.
        self.epsilon_decay = 0.995
        # min epsilon of ε-greedy.
        self.epsilon_min = 0.01

        # actor learning rate
        self.a_lr = 0.0001
        # critic learining rate
        self.c_lr = 0.001

        # ddpg model
        self.actor = self._build_actor()
        self.critic = self._build_critic()

        # target model
        self.target_actor = self._build_actor()
        self.target_actor.set_weights(self.actor.get_weights())
        self.target_critic = self._build_critic()
        self.target_critic.set_weights(self.critic.get_weights())

        # gradient function

        self.get_critic_grad = self.critic_gradient()
        self.actor_optimizer()

    def load(self):
        if os.path.exists('model/ddpg_actor.h5') and os.path.exists('model/ddpg_critic.h5'):
            self.actor.load_weights('model/ddpg_actor.h5')
            self.critic.load_weights('model/ddpg_critic.h5')

    def _build_actor(self):
        """Actor model.
        """
        inputs = Input(shape=(3,), name='state_input')
        x = Dense(40, activation='relu')(inputs)
        x = Dense(40, activation='relu')(x)
        x = Dense(1, activation='tanh')(x)
        output = Lambda(lambda x: x * self.bound)(x)

        model = Model(inputs=inputs, outputs=output)
        model.compile(loss='mse', optimizer=Adam(lr=self.a_lr))

        return model

    def _build_critic(self):
        """Critic model.
        """
        sinput = Input(shape=(3,), name='state_input')
        ainput = Input(shape=(1,), name='action_input')
        s = Dense(40, activation='relu')(sinput)
        a = Dense(40, activation='relu')(ainput)
        x = concatenate([s, a])
        x = Dense(40, activation='relu')(x)
        output = Dense(1, activation='linear')(x)

        model = Model(inputs=[sinput, ainput], outputs=output)
        model.compile(loss='mse', optimizer=Adam(lr=self.c_lr))

        return model

    def actor_optimizer(self):
        """actor_optimizer.

        Returns:
            function, opt function for actor.
        """
        self.ainput = self.actor.input
        aoutput = self.actor.output
        trainable_weights = self.actor.trainable_weights
        self.action_gradient = tf.placeholder(tf.float32, shape=(None, 1))

        # tf.gradients will calculate dy/dx with a initial gradients for y
        # action_gradient is dq / da, so this is dq/da * da/dparams
        params_grad = tf.gradients(aoutput, trainable_weights, -self.action_gradient)
        grads = zip(params_grad, trainable_weights)
        self.opt = tf.train.AdamOptimizer(self.a_lr).apply_gradients(grads)
        self.sess.run(tf.global_variables_initializer())

    def critic_gradient(self):
        """get critic gradient function.

        Returns:
            function, gradient function for critic.
        """
        cinput = self.critic.input
        coutput = self.critic.output

        # compute the gradient of the action with q value, dq/da.
        action_grads = K.gradients(coutput, cinput[1])

        return K.function([cinput[0], cinput[1]], action_grads)

    def OU(self, x, mu=0, theta=0.15, sigma=0.2):
        """Ornstein-Uhlenbeck process.
        formula：ou = θ * (μ - x) + σ * w

        Arguments:
            x: action value.
            mu: μ, mean fo values.
            theta: θ, rate the variable reverts towards to the mean. 
            sigma：σ, degree of volatility of the process.

        Returns:
            OU value
        """
        return theta * (mu - x) + sigma * np.random.randn(1)

    def get_action(self, X):
        """get actor action with ou noise.

        Arguments:
            X: state value.
        """
        action = self.actor.predict(X)[0][0]

        # add randomness to action selection for exploration
        noise = max(self.epsilon, 0) * self.OU(action)
        action = np.clip(action + noise, -self.bound, self.bound)

        return action

    def remember(self, state, action, reward, next_state, done):
        """add data to experience replay.

        Arguments:
            state: observation.
            action: action.
            reward: reward.
            next_state: next_observation.
            done: if game done.
        """
        item = (state, action, reward, next_state, done)
        self.memory_buffer.append(item)

    def update_epsilon(self):
        """update epsilon.
        """
        if self.epsilon >= self.epsilon_min:
            self.epsilon *= self.epsilon_decay

    def process_batch(self, batch):
        """process batch data.

        Arguments:
            batch: batch size.

        Returns:
            states: states.
            actions: actions.
            y: Q_value.
        """
        y = []
         # ranchom choice batch data from experience replay.
        data = random.sample(self.memory_buffer, batch)
        states = np.array([d[0] for d in data])
        actions = np.array([d[1] for d in data])
        next_states = np.array([d[3] for d in data])

        # Q_target。
        next_actions = self.target_actor.predict(next_states)
        q = self.target_critic.predict([next_states, next_actions])

        # update Q value
        for i, (_, _, reward, _, done) in enumerate(data):
            target = reward
            if not done:
                target += self.gamma * q[i][0]
            y.append(target)

        return states, actions, y

    def update_model(self, X1, X2, y):
        """update ddpg model.

        Arguments:
            states: states.
            actions: actions.
            y: Q_value.

        Returns:
            loss: critic loss.
        """
#        loss = self.critic.train_on_batch([X1, X2], y)
        loss = self.critic.fit([X1, X2], y, verbose=0)
        loss = np.mean(loss.history['loss'])

        X3 = self.actor.predict(X1)
        a_grads = np.array(self.get_critic_grad([X1, X3]))[0]
        self.sess.run(self.opt, feed_dict={
            self.ainput: X1,
            self.action_gradient: a_grads
        })

        return loss

    def update_target_model(self):
        """soft update target model.
        formula：θ​​t ← τ * θ + (1−τ) * θt, τ << 1. 
        """
        critic_weights = self.critic.get_weights()
        actor_weights = self.actor.get_weights()
        critic_target_weights = self.target_critic.get_weights()
        actor_target_weights = self.target_actor.get_weights()

        for i in range(len(critic_weights)):
            critic_target_weights[i] = self.TAU * critic_weights[i] + (1 - self.TAU) * critic_target_weights[i]

        for i in range(len(actor_weights)):
            actor_target_weights[i] = self.TAU * actor_weights[i] + (1 - self.TAU) * actor_target_weights[i]

        self.target_critic.set_weights(critic_target_weights)
        self.target_actor.set_weights(actor_target_weights)

    def train(self, episode, batch):
        """training model.
        Arguments:
            episode: ganme episode.
            batch： batch size of episode.

        Returns:
            history: training history.
        """
        history = {'episode': [], 'Episode_reward': [], 'Loss': []}

        for i in range(episode):
            observation = self.env.reset()
            reward_sum = 0
            losses = []

            for j in range(200):
                # chocie action from ε-greedy.
                x = observation.reshape(-1, 3)

                # actor action
                action = self.get_action(x)
                observation, reward, done, _ = self.env.step(action)
                # add data to experience replay.
                reward_sum += reward
                self.remember(x[0], action, reward, observation, done)

                if len(self.memory_buffer) > batch:
                    X1, X2, y = self.process_batch(batch)

                    # update DDPG model
                    loss = self.update_model(X1, X2, y)
                    # update target model
                    self.update_target_model()
                    # reduce epsilon pure batch.
                    self.update_epsilon()

                    losses.append(loss)

            loss = np.mean(losses)
            history['episode'].append(i)
            history['Episode_reward'].append(reward_sum)
            history['Loss'].append(loss)

            print('Episode: {}/{} | reward: {} | loss: {:.3f}'.format(i, episode, reward_sum, loss))

        self.actor.save_weights('model/ddpg_actor.h5')
        self.critic.save_weights('model/ddpg_critic.h5')

        return history

    def play(self):
        """play game with model.
        """
        print('play...')
        observation = self.env.reset()

        reward_sum = 0
        random_episodes = 0

        while random_episodes < 10:
            self.env.render()

            x = observation.reshape(-1, 3)
            action = self.actor.predict(x)[0]
            observation, reward, done, _ = self.env.step(action)

            reward_sum += reward

            if done:
                print("Reward for this episode was: {}".format(reward_sum))
                random_episodes += 1
                reward_sum = 0
                observation = self.env.reset()

        self.env.close()


if __name__ == '__main__':
    model = DDPG()

    history = model.train(200, 128)
    model.save_history(history, 'ddpg.csv')
    
    model.load()
    model.play()


================================================
FILE: DQN.py
================================================
# -*- coding: utf-8 -*-
import os
import random
import numpy as np

from collections import deque

from keras.layers import Input, Dense
from keras.models import Model
from keras.optimizers import Adam

from DRL import DRL


class DQN(DRL):
    """Deep Q-Learning.
    """
    def __init__(self):
        super(DQN, self).__init__()

        self.model = self.build_model()

        # experience replay.
        self.memory_buffer = deque(maxlen=2000)
        # discount rate for q value.
        self.gamma = 0.95
        # epsilon of ε-greedy.
        self.epsilon = 1.0
        # discount rate for epsilon.
        self.epsilon_decay = 0.995
        # min epsilon of ε-greedy.
        self.epsilon_min = 0.01

    def load(self):
        if os.path.exists('model/dqn.h5'):
            self.model.load_weights('model/dqn.h5')

    def build_model(self):
        """basic model.
        """
        inputs = Input(shape=(4,))
        x = Dense(16, activation='relu')(inputs)
        x = Dense(16, activation='relu')(x)
        x = Dense(2, activation='linear')(x)

        model = Model(inputs=inputs, outputs=x)

        model.compile(loss='mse', optimizer=Adam(1e-3))

        return model

    def egreedy_action(self, state):
        """ε-greedy
        Arguments:
            state: observation

        Returns:
            action: action
        """
        if np.random.rand() <= self.epsilon:
             return random.randint(0, 1)
        else:
            q_values = self.model.predict(state)[0]
            return np.argmax(q_values)

    def remember(self, state, action, reward, next_state, done):
        """add data to experience replay.
        Arguments:
            state: observation
            action: action
            reward: reward
            next_state: next_observation
            done: if game done.
        """
        item = (state, action, reward, next_state, done)
        self.memory_buffer.append(item)

    def update_epsilon(self):
        """update epsilon
        """
        if self.epsilon >= self.epsilon_min:
            self.epsilon *= self.epsilon_decay

    def process_batch(self, batch):
        """process batch data
        Arguments:
            batch: batch size

        Returns:
            X: states
            y: [Q_value1, Q_value2]
        """
         # ranchom choice batch data from experience replay.
        data = random.sample(self.memory_buffer, batch)
        # Q_target。
        states = np.array([d[0] for d in data])
        next_states = np.array([d[3] for d in data])

        y = self.model.predict(states)
        q = self.model.predict(next_states)

        for i, (_, action, reward, _, done) in enumerate(data):
            target = reward
            if not done:
                target += self.gamma * np.amax(q[i])
            y[i][action] = target

        return states, y


    def train(self, episode, batch):
        """training 
        Arguments:
            episode: game episode
            batch： batch size

        Returns:
            history: training history
        """
        history = {'episode': [], 'Episode_reward': [], 'Loss': []}

        count = 0
        for i in range(episode):
            observation = self.env.reset()
            reward_sum = 0
            loss = np.infty
            done = False

            while not done:
                # chocie action from ε-greedy.
                x = observation.reshape(-1, 4)
                action = self.egreedy_action(x)
                observation, reward, done, _ = self.env.step(action)
                # add data to experience replay.
                reward_sum += reward
                self.remember(x[0], action, reward, observation, done)

                if len(self.memory_buffer) > batch:
                    X, y = self.process_batch(batch)
                    loss = self.model.train_on_batch(X, y)

                    count += 1
                    # reduce epsilon pure batch.
                    self.update_epsilon()

            if i % 5 == 0:
                history['episode'].append(i)
                history['Episode_reward'].append(reward_sum)
                history['Loss'].append(loss)

                print('Episode: {} | Episode reward: {} | loss: {:.3f} | e:{:.2f}'.format(i, reward_sum, loss, self.epsilon))

        self.model.save_weights('model/dqn.h5')

        return history


if __name__ == '__main__':
    model = DQN()

    history = model.train(600, 32)
    model.save_history(history, 'dqn.csv')
    
    model.load()
    model.play()


================================================
FILE: DRL.py
================================================
# -*- coding: utf-8 -*-
import os
import gym
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


class DRL:
    def __init__(self):
        self.env = gym.make('CartPole-v0')

        if not os.path.exists('model'):
            os.mkdir('model')

        if not os.path.exists('history'):
            os.mkdir('history')

    def play(self, m='pg'):
        """play game with model.
        """
        print('play...')
        observation = self.env.reset()

        reward_sum = 0
        random_episodes = 0

        while random_episodes < 10:
            self.env.render()

            x = observation.reshape(-1, 4)
            if m == 'pg':
                prob = self.model.predict(x)[0][0]
                action = 1 if prob > 0.5 else 0
            elif m == 'acs':
                prob = self.actor.predict(x)[0][0]
                action = 1 if prob > 0.5 else 0
            else:
                action = np.argmax(self.model.predict(x)[0])
            observation, reward, done, _ = self.env.step(action)

            reward_sum += reward

            if done:
                print("Reward for this episode was: {}".format(reward_sum))
                random_episodes += 1
                reward_sum = 0
                observation = self.env.reset()

        self.env.close()

    def plot(self, history):
        x = history['episode']
        r = history['Episode_reward']
        l = history['Loss']

        fig = plt.figure()
        ax = fig.add_subplot(121)
        ax.plot(x, r)
        ax.set_title('Episode_reward')
        ax.set_xlabel('episode')
        ax = fig.add_subplot(122)
        ax.plot(x, l)
        ax.set_title('Loss')
        ax.set_xlabel('episode')

        plt.show()

    def save_history(self, history, name):
        name = os.path.join('history', name)

        df = pd.DataFrame.from_dict(history)
        df.to_csv(name, index=False, encoding='utf-8')


================================================
FILE: DoubleDQN.py
================================================
# -*- coding: utf-8 -*-
import os
import random
import numpy as np

from DQN import DQN


class DDQN(DQN):
    """Nature Deep Q-Learning.
    """
    def __init__(self):
        super(DDQN, self).__init__()

        self.model = self.build_model()
        self.target_model = self.build_model()
        self.update_target_model()

    def load(self):
        if os.path.exists('model/ddqn.h5'):
            self.model.load_weights('model/ddqn.h5')

    def update_target_model(self):
        """update target_model
        """
        self.target_model.set_weights(self.model.get_weights())

    def process_batch(self, batch):
        """process batch data
        Arguments:
            batch: batch size

        Returns:
            X: states
            y: [Q_value1, Q_value2]
        """
         # ranchom choice batch data from experience replay.
        data = random.sample(self.memory_buffer, batch)
        # Q_target。
        states = np.array([d[0] for d in data])
        next_states = np.array([d[3] for d in data])

        y = self.model.predict(states)
        q = self.target_model.predict(next_states)
        next_action = np.argmax(self.model.predict(next_states), axis=1)

        for i, (_, action, reward, _, done) in enumerate(data):
            target = reward
            if not done:
                target += self.gamma * q[i][next_action[i]]
            y[i][action] = target

        return states, y

    def train(self, episode, batch):
        """training 
        Arguments:
            episode: game episode
            batch： batch size

        Returns:
            history: training history
        """
        history = {'episode': [], 'Episode_reward': [], 'Loss': []}

        count = 0
        for i in range(episode):
            observation = self.env.reset()
            reward_sum = 0
            loss = np.infty
            done = False

            while not done:
                # chocie action from ε-greedy.
                x = observation.reshape(-1, 4)
                action = self.egreedy_action(x)
                observation, reward, done, _ = self.env.step(action)
                # add data to experience replay.
                reward_sum += reward
                self.remember(x[0], action, reward, observation, done)

                if len(self.memory_buffer) > batch:
                    X, y = self.process_batch(batch)
                    loss = self.model.train_on_batch(X, y)

                    count += 1
                    # reduce epsilon pure batch.
                    self.update_epsilon()

                    # update target_model every 20 episode
                    if count != 0 and count % 20 == 0:
                        self.update_target_model()

            if i % 5 == 0:
                history['episode'].append(i)
                history['Episode_reward'].append(reward_sum)
                history['Loss'].append(loss)

                print('Episode: {} | Episode reward: {} | loss: {:.3f} | e:{:.2f}'.format(i, reward_sum, loss, self.epsilon))

        self.model.save_weights('model/ddqn.h5')

        return history


if __name__ == '__main__':
    model = DDQN()

    history = model.train(600, 32)
    model.save_history(history, 'ddqn.csv')

    model.load()
    model.play('dqn')


================================================
FILE: DuelingDQN.py
================================================
# -*- coding: utf-8 -*-
import os
import numpy as np

from keras.layers import Input, Dense, Add, Subtract, Lambda
from keras.models import Model
from keras.optimizers import Adam
import keras.backend as K

from NatureDQN import NDQN


class DuelingDQN(NDQN):
    """Dueling DQN.
    """
    def __init__(self):
        super(DuelingDQN, self).__init__()

    def load(self):
        if os.path.exists('model/dueling.h5'):
            self.model.load_weights('model/dueling.h5')

    def build_model(self):
        """basic model.
        """
        inputs = Input(shape=(4,))
        x = Dense(16, activation='relu')(inputs)
        x = Dense(16, activation='relu')(x)

        value = Dense(2, activation='linear')(x)
        a = Dense(2, activation='linear')(x)
        meam = Lambda(lambda x: K.mean(x, axis=1, keepdims=True))(a)
        advantage = Subtract()([a, meam])

        q = Add()([value, advantage])

        model = Model(inputs=inputs, outputs=q)

        model.compile(loss='mse', optimizer=Adam(1e-3))

        return model

    def train(self, episode, batch):
        """training 
        Arguments:
            episode: game episode
            batch： batch size

        Returns:
            history: training history
        """
        history = {'episode': [], 'Episode_reward': [], 'Loss': []}

        count = 0
        for i in range(episode):
            observation = self.env.reset()
            reward_sum = 0
            loss = np.infty
            done = False

            while not done:
                # chocie action from ε-greedy.
                x = observation.reshape(-1, 4)
                action = self.egreedy_action(x)
                observation, reward, done, _ = self.env.step(action)
                # add data to experience replay.
                reward_sum += reward
                self.remember(x[0], action, reward, observation, done)

                if len(self.memory_buffer) > batch:
                    X, y = self.process_batch(batch)
                    loss = self.model.train_on_batch(X, y)

                    count += 1
                    # reduce epsilon pure batch.
                    self.update_epsilon()

                    # update target_model every 20 episode
                    if count != 0 and count % 20 == 0:
                        self.update_target_model()

            if i % 5 == 0:
                history['episode'].append(i)
                history['Episode_reward'].append(reward_sum)
                history['Loss'].append(loss)

                print('Episode: {} | Episode reward: {} | loss: {:.3f} | e:{:.2f}'.format(i, reward_sum, loss, self.epsilon))

        self.model.save_weights('model/dueling.h5')

        return history


if __name__ == '__main__':
    model = DuelingDQN()

    history = model.train(600, 32)
    model.save_history(history, 'dueling.csv')

    model.load()
    model.play('dqn')


================================================
FILE: NatureDQN.py
================================================
# -*- coding: utf-8 -*-
import os
import random
import numpy as np

from DQN import DQN


class NDQN(DQN):
    """Nature Deep Q-Learning.
    """
    def __init__(self):
        super(NDQN, self).__init__()

        self.model = self.build_model()
        self.target_model = self.build_model()
        self.update_target_model()

    def load(self):
        if os.path.exists('model/ndqn.h5'):
            self.model.load_weights('model/ndqn.h5')

    def update_target_model(self):
        """update target_model
        """
        self.target_model.set_weights(self.model.get_weights())

    def process_batch(self, batch):
        """process batch data
        Arguments:
            batch: batch size

        Returns:
            X: states
            y: [Q_value1, Q_value2]
        """
         # ranchom choice batch data from experience replay.
        data = random.sample(self.memory_buffer, batch)
        # Q_target。
        states = np.array([d[0] for d in data])
        next_states = np.array([d[3] for d in data])

        y = self.model.predict(states)
        q = self.target_model.predict(next_states)

        for i, (_, action, reward, _, done) in enumerate(data):
            target = reward
            if not done:
                target += self.gamma * np.amax(q[i])
            y[i][action] = target

        return states, y

    def train(self, episode, batch):
        """training 
        Arguments:
            episode: game episode
            batch： batch size

        Returns:
            history: training history
        """
        history = {'episode': [], 'Episode_reward': [], 'Loss': []}

        count = 0
        for i in range(episode):
            observation = self.env.reset()
            reward_sum = 0
            loss = np.infty
            done = False

            while not done:
                # chocie action from ε-greedy.
                x = observation.reshape(-1, 4)
                action = self.egreedy_action(x)
                observation, reward, done, _ = self.env.step(action)
                # add data to experience replay.
                reward_sum += reward
                self.remember(x[0], action, reward, observation, done)

                if len(self.memory_buffer) > batch:
                    X, y = self.process_batch(batch)
                    loss = self.model.train_on_batch(X, y)

                    count += 1
                    # reduce epsilon pure batch.
                    self.update_epsilon()

                    # update target_model every 20 episode
                    if count != 0 and count % 20 == 0:
                        self.update_target_model()

            if i % 5 == 0:
                history['episode'].append(i)
                history['Episode_reward'].append(reward_sum)
                history['Loss'].append(loss)

                print('Episode: {} | Episode reward: {} | loss: {:.3f} | e:{:.2f}'.format(i, reward_sum, loss, self.epsilon))

        self.model.save_weights('model/ndqn.h5')

        return history


if __name__ == '__main__':
    model = NDQN()

    history = model.train(600, 32)
    model.save_history(history, 'ndqn.csv')

    model.load()
    model.play('dqn')


================================================
FILE: PPO_TF.py
================================================
import os
import gym
import numpy as np
import pandas as pd
import tensorflow as tf


class PPO:
    def __init__(self, ep, batch, t='ppo2'):
        self.t = t
        self.ep = ep
        self.batch = batch
        self.log = 'model/{}_log'.format(t)

        self.env = gym.make('Pendulum-v0')
        self.bound = self.env.action_space.high[0]

        self.gamma = 0.9
        self.A_LR = 0.0001
        self.C_LR = 0.0002
        self.A_UPDATE_STEPS = 10
        self.C_UPDATE_STEPS = 10

        # KL penalty, d_target、β for ppo1
        self.kl_target = 0.01
        self.lam = 0.5
        # ε for ppo2
        self.epsilon = 0.2

        self.sess = tf.Session()
        self.build_model()

    def _build_critic(self):
        """critic model.
        """
        with tf.variable_scope('critic'):
            x = tf.layers.dense(self.states, 100, tf.nn.relu)

            self.v = tf.layers.dense(x, 1)
            self.advantage = self.dr - self.v

    def _build_actor(self, name, trainable):
        """actor model.
        """
        with tf.variable_scope(name):
            x = tf.layers.dense(self.states, 100, tf.nn.relu, trainable=trainable)

            mu = self.bound * tf.layers.dense(x, 1, tf.nn.tanh, trainable=trainable)
            sigma = tf.layers.dense(x, 1, tf.nn.softplus, trainable=trainable)

            norm_dist = tf.distributions.Normal(loc=mu, scale=sigma)

        params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name)

        return norm_dist, params

    def build_model(self):
        """build model with ppo loss.
        """
        # inputs
        self.states = tf.placeholder(tf.float32, [None, 3], 'states')
        self.action = tf.placeholder(tf.float32, [None, 1], 'action')
        self.adv = tf.placeholder(tf.float32, [None, 1], 'advantage')
        self.dr = tf.placeholder(tf.float32, [None, 1], 'discounted_r')

        # build model
        self._build_critic()
        nd, pi_params = self._build_actor('actor', trainable=True)
        old_nd, oldpi_params = self._build_actor('old_actor', trainable=False)

        # define ppo loss
        with tf.variable_scope('loss'):
            # critic loss
            self.closs = tf.reduce_mean(tf.square(self.advantage))

            # actor loss
            with tf.variable_scope('surrogate'):
                ratio = tf.exp(nd.log_prob(self.action) - old_nd.log_prob(self.action))
                surr = ratio * self.adv

            if self.t == 'ppo1':
                self.tflam = tf.placeholder(tf.float32, None, 'lambda')
                kl = tf.distributions.kl_divergence(old_nd, nd)
                self.kl_mean = tf.reduce_mean(kl)
                self.aloss = -(tf.reduce_mean(surr - self.tflam * kl))
            else: 
                self.aloss = -tf.reduce_mean(tf.minimum(
                    surr,
                    tf.clip_by_value(ratio, 1.- self.epsilon, 1.+ self.epsilon) * self.adv))

        # define Optimizer
        with tf.variable_scope('optimize'):
            self.ctrain_op = tf.train.AdamOptimizer(self.C_LR).minimize(self.closs)
            self.atrain_op = tf.train.AdamOptimizer(self.A_LR).minimize(self.aloss)

        with tf.variable_scope('sample_action'):
            self.sample_op = tf.squeeze(nd.sample(1), axis=0)

        # update old actor
        with tf.variable_scope('update_old_actor'):
            self.update_old_actor = [oldp.assign(p) for p, oldp in zip(pi_params, oldpi_params)]

        tf.summary.FileWriter(self.log, self.sess.graph)

        self.sess.run(tf.global_variables_initializer())

    def choose_action(self, state):
        """choice continuous action from normal distributions.

        Arguments:
            state: state.

        Returns:
           action.
        """
        state = state[np.newaxis, :]
        action = self.sess.run(self.sample_op, {self.states: state})[0]

        return np.clip(action, -self.bound, self.bound)

    def get_value(self, state):
        """get q value.

        Arguments:
            state: state.

        Returns:
           q_value.
        """
        if state.ndim < 2: state = state[np.newaxis, :]

        return self.sess.run(self.v, {self.states: state})

    def discount_reward(self, states, rewards, next_observation):
        """Compute target value.

        Arguments:
            states: state in episode.
            rewards: reward in episode.
            next_observation: state of last action.

        Returns:
            targets: q targets.
        """
        s = np.vstack([states, next_observation.reshape(-1, 3)])
        q_values = self.get_value(s).flatten()

        targets = rewards + self.gamma * q_values[1:]
        targets = targets.reshape(-1, 1)

        return targets

# not work.
#    def neglogp(self, mean, std, x):
#        """Gaussian likelihood
#        """
#        return 0.5 * tf.reduce_sum(tf.square((x - mean) / std), axis=-1) \
#               + 0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(x)[-1]) \
#               + tf.reduce_sum(tf.log(std), axis=-1)

    def update(self, states, action, dr):
        """update model.

        Arguments:
            states: states.
            action: action of states.
            dr: discount reward of action.
        """
        self.sess.run(self.update_old_actor)

        adv = self.sess.run(self.advantage,
                            {self.states: states,
                             self.dr: dr})

        # update actor
        if self.t == 'ppo1':
            # run ppo1 loss
            for _ in range(self.A_UPDATE_STEPS):
                _, kl = self.sess.run(
                    [self.atrain_op, self.kl_mean],
                    {self.states: states,
                     self.action: action,
                     self.adv: adv,
                     self.tflam: self.lam})

            if kl < self.kl_target / 1.5:
                self.lam /= 2
            elif kl > self.kl_target * 1.5:
                self.lam *= 2
        else:
            # run ppo2 loss
            for _ in range(self.A_UPDATE_STEPS):
                self.sess.run(self.atrain_op,
                              {self.states: states,
                               self.action: action,
                               self.adv: adv})

        # update critic
        for _ in range(self.C_UPDATE_STEPS):
            self.sess.run(self.ctrain_op,
                          {self.states: states,
                           self.dr: dr})

    def train(self):
        """train method.
        """
        tf.reset_default_graph()

        history = {'episode': [], 'Episode_reward': []}

        for i in range(self.ep):
            observation = self.env.reset()

            states, actions, rewards = [], [], []
            episode_reward = 0
            j = 0

            while True:
                a = self.choose_action(observation)
                next_observation, reward, done, _ = self.env.step(a)
                states.append(observation)
                actions.append(a)

                episode_reward += reward
                rewards.append((reward + 8) / 8)

                observation = next_observation

                if (j + 1) % self.batch == 0:
                    states = np.array(states)
                    actions = np.array(actions)
                    rewards = np.array(rewards)
                    d_reward = self.discount_reward(states, rewards, next_observation)

                    self.update(states, actions, d_reward)

                    states, actions, rewards = [], [], []

                if done:
                    break
                j += 1

            history['episode'].append(i)
            history['Episode_reward'].append(episode_reward)
            print('Episode: {} | Episode reward: {:.2f}'.format(i, episode_reward))

        return history

    def save_history(self, history, name):
        name = os.path.join('history', name)

        df = pd.DataFrame.from_dict(history)
        df.to_csv(name, index=False, encoding='utf-8')


if __name__ == '__main__':
    model = PPO(1000, 32, 'ppo2')
    history = model.train()
    model.save_history(history, 'ppo2.csv')


================================================
FILE: PolicyNetwork.py
================================================
# -*- coding: utf-8 -*-
import os

import numpy as np

from keras.layers import Input, Dense
from keras.models import Model
from keras.optimizers import Adam
import keras.backend as K

from DRL import DRL


class PolicyNetwork(DRL):
    """Policy Gradient Algorithms(Policy Network)
    """
    def __init__(self):
        super(PolicyNetwork, self).__init__()

        self.model = self._build_model()
        self.gamma = 0.95

    def load(self):
        if os.path.exists('model/pg.h5'):
            self.model.load_weights('model/pg.h5')

    def _build_model(self):
        """basic model.
        """
        inputs = Input(shape=(4,), name='ob_input')
        x = Dense(16, activation='relu')(inputs)
        x = Dense(16, activation='relu')(x)
        x = Dense(1, activation='sigmoid')(x)

        model = Model(inputs=inputs, outputs=x)

        return model

    def loss(self, y_true, y_pred):
        """loss function.
        Arguments:
            y_true: (action, reward)
            y_pred: action_prob

        Returns:
            loss: reward loss
        """
        action_pred = y_pred
        action_true, discount_episode_reward = y_true[:, 0], y_true[:, 1]

        action_true = K.reshape(action_true, (-1, 1))
        loss = K.binary_crossentropy(action_true, action_pred)
        loss = loss * K.flatten(discount_episode_reward)

        return loss

    def discount_reward(self, rewards):
        """Discount reward
        Arguments:
            rewards: rewards in a episode.
        """
        # compute the discounted reward backwards through time.
        discount_rewards = np.zeros_like(rewards, dtype=np.float32)
        cumulative = 0.
        for i in reversed(range(len(rewards))):
            cumulative = cumulative * self.gamma + rewards[i]
            discount_rewards[i] = cumulative

        # size the rewards to be unit normal (helps control the gradient estimator variance).
        discount_rewards -= np.mean(discount_rewards)
        discount_rewards //= np.std(discount_rewards)

        return list(discount_rewards)

    def train(self, episode, batch):
        """training model.
        Arguments:
            episode: ganme episode
            batch： batch size of episode

        Returns:
            history: training history
        """
        self.model.compile(loss=self.loss, optimizer=Adam(lr=0.01))

        history = {'episode': [], 'Episode_reward': [], 'Loss': []}

        episode_reward = 0
        states = []
        actions = []
        rewards = []
        discount_rewards = []

        for i in range(episode):
            observation = self.env.reset()
            erewards = []

            while True:
                x = observation.reshape(-1, 4)
                prob = self.model.predict(x)[0][0]

                # choice action with prob.
                action = np.random.choice(np.array(range(2)), size=1, p=[1 - prob, prob])[0]
                observation, reward, done, _ = self.env.step(action)

                states.append(x[0])
                actions.append(action)
                erewards.append(reward)
                rewards.append(reward)

                if done:
                    # calculate discount rewards every episode.
                    discount_rewards.extend(self.discount_reward(erewards))
                    break

            if i != 0 and i % batch == 0: 
                episode_reward = sum(rewards) / batch

                X = np.array(states)
                y = np.array(list(zip(actions, discount_rewards)))

                loss = self.model.train_on_batch(X, y)

                history['episode'].append(i)
                history['Episode_reward'].append(episode_reward)
                history['Loss'].append(loss)

                print('Episode: {} | Episode reward: {} | loss: {:.3f}'.format(i, episode_reward, loss))

                episode_reward = 0
                states = []
                actions = []
                rewards = []
                discount_rewards = []

        self.model.save_weights('model/pg.h5')

        return history


if __name__ == '__main__':
    model = PolicyNetwork()

    history = model.train(5000, 5)
    model.save_history(history, 'pg.csv')
    
    model.load()
    model.play()


================================================
FILE: README.md
================================================
# Deep-Reinforcement-Learning-Practice
Practice of Deep Reinforcement Learning with Keras and gym.

Continuous updating...


## Algorithm


| #	| Name  | Paper |
| - | ----- | :--------: |
| 1 | [DQN](DQN.py) | [Playing Atari with Deep Reinforcement Learning](http://cn.arxiv.org/abs/1312.5602)|
| 2 | [Nature DQN](NatureDQN.py) | [Human-level control through deep reinforcement learning](https://www.nature.com/articles/nature14236) |
| 3 | [Double DQN](DoubleDQN.py) | [Deep Reinforcement Learning with Double Q-learning](http://cn.arxiv.org/abs/1509.06461v1)  |
| 4 | [Dueling DQN](DuelingDQN.py) | [Dueling Network Architectures for Deep Reinforcement Learning](https://arxiv.org/pdf/1511.06581.pdf) |
| 5 | [Actor-Critic](AC_sparse.py) | [Actor-Critic Algorithms](https://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf) |
| 6 | [Policy Network](PolicyNetwork.py) | [Policy gradient methods for reinforcement learning with function approximation](https://www.researchgate.net/publication/2503757_Policy_Gradient_Methods_for_Reinforcement_Learning_with_Function_Approximation) |
| 7 | [DDPG](DDPG.py) | [Continuous Control with Deep Reinforcement Learning](https://arxiv.org/abs/1509.02971) |
| 8 | [PPO_TF](PPO_TF.py) | [Proximal Policy Optimization Algorithms](https://arxiv.org/abs/1707.06347) |
| 9 | [A3C](A3C_sparse.py) | [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/pdf/1602.01783.pdf) |


================================================
FILE: game/CartPole.py
================================================
# -*- coding: utf-8 -*-
import gym
import numpy as np


def try_gym():
    # creat CartPole env.
    env = gym.make('CartPole-v0')
    # reset game env.
    env.reset()

    # episodes of game
    random_episodes = 0
    # sum of reward of game per episode
    reward_sum = 0

    while random_episodes < 10:
        # show game
        env.render()
        # random choice a action
        # execute the action
        observation, reward, done, _ = env.step(np.random.randint(0, 2))
        reward_sum += reward
        # print result and reset ganme env if game done.
        if done:
            random_episodes += 1
            print("Reward for this episode was: {}".format(reward_sum))
            reward_sum = 0
            env.reset()

    env.close()


if __name__ == '__main__':
    try_gym()


================================================
FILE: game/Pendulum.py
================================================
# -*- coding: utf-8 -*-
import gym


def try_gym():
    # creat Pendulum env.
    env = gym.make('Pendulum-v0')
    # reset game env.
    env.reset()

    # episodes of game
    random_episodes = 0
    # sum of reward of game per episode
    reward_sum = 0

    while random_episodes < 10:
        # show game
        env.render()
        # random choice a action
        # execute the action
        action = env.action_space.sample()
        observation, reward, done, _ = env.step(action)
        reward_sum += reward
        # print result and reset ganme env if game done.
        if done:
            random_episodes += 1
            print("Reward for this episode was: {}".format(reward_sum))
            reward_sum = 0
            env.reset()

    env.close()


if __name__ == '__main__':
    try_gym()


================================================
FILE: history/a3c_sparse.csv
================================================
episode,Episode_reward
0,11.0
1,17.0
2,17.0
3,13.0
4,14.0
5,22.0
6,34.0
7,14.0
8,20.0
9,25.0
10,14.0
11,12.0
12,14.0
13,15.0
14,11.0
15,21.0
16,33.0
17,18.0
18,20.0
19,34.0
20,13.0
21,25.0
22,21.0
23,17.0
24,19.0
25,26.0
26,36.0
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28,31.0
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519,181.0
520,136.0
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================================================
FILE: history/ac_continue.csv
================================================
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================================================
FILE: history/ac_sparse.csv
================================================
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================================================
FILE: history/ddpg.csv
================================================
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-116.65599103879406,11.857215057630093,188
-119.57400403798566,12.931360413730145,189
-126.60727857129913,10.384384954106062,190
-130.69187463526168,11.276750937402248,191
-532.6094861649789,9.913348460327834,192
-119.40447754106185,10.843797551989555,193
-118.01457640625543,15.639693554751576,194
-125.38472296989859,14.797974425330757,195
-0.7717357914055901,9.847735727513209,196
-117.80949351393008,13.156731079723686,197
-117.81753855241199,8.287331903707235,198
-244.8041840890352,8.84490654880181,199


================================================
FILE: history/ddqn.csv
================================================
Episode_reward,Loss,episode
11.0,inf,0
12.0,0.4681931138038635,5
15.0,1.190276861190796,10
12.0,1.9253158569335938,15
14.0,2.1662869453430176,20
14.0,0.3513646721839905,25
13.0,1.5280985832214355,30
9.0,7.641080379486084,35
13.0,5.177434921264648,40
37.0,0.32416990399360657,45
30.0,3.0215706825256348,50
36.0,2.573920726776123,55
47.0,2.4598228931427,60
47.0,0.20579954981803894,65
59.0,0.9766868948936462,70
114.0,0.021512869745492935,75
78.0,0.020475244149565697,80
141.0,0.018932819366455078,85
200.0,0.31822460889816284,90
198.0,0.03797835111618042,95
191.0,0.014194854535162449,100
182.0,0.013047108426690102,105
176.0,2.1590566635131836,110
155.0,0.4268774092197418,115
157.0,0.024365421384572983,120
150.0,0.010818028822541237,125
143.0,0.6679432392120361,130
137.0,0.012195337563753128,135
139.0,0.016057107597589493,140
133.0,0.006115872412919998,145
130.0,0.011953135952353477,150
139.0,0.00645385030657053,155
147.0,0.014706989750266075,160
150.0,0.03395920991897583,165
132.0,0.006384558044373989,170
147.0,0.0018864702433347702,175
168.0,0.006037265993654728,180
147.0,0.003880517091602087,185
145.0,0.0685364305973053,190
139.0,0.039683014154434204,195
147.0,0.026838181540369987,200
148.0,0.0033504890743643045,205
153.0,0.027849620208144188,210
148.0,0.0022808443754911423,215
142.0,0.004483409691601992,220
153.0,0.012921614572405815,225
178.0,0.021095992997288704,230
145.0,0.016173984855413437,235
180.0,0.005053142085671425,240
189.0,2.571658134460449,245
200.0,0.005489872302860022,250
200.0,0.003300785319879651,255
200.0,0.010993970558047295,260
200.0,0.02414834313094616,265
200.0,0.0550992377102375,270
200.0,0.028599431738257408,275
200.0,0.04435380548238754,280
200.0,0.07636132836341858,285
200.0,0.02288103848695755,290
161.0,0.026148678734898567,295
182.0,0.046217601746320724,300
200.0,0.09286589920520782,305
200.0,0.027517562732100487,310
200.0,0.03821772336959839,315
200.0,0.013089239597320557,320
200.0,5.8109049797058105,325
11.0,0.007258770987391472,330
200.0,0.09761332720518112,335
200.0,3.223811388015747,340
200.0,0.03628034517168999,345
200.0,0.05087439715862274,350
200.0,0.029234319925308228,355
200.0,0.013562529347836971,360
200.0,5.093896865844727,365
200.0,0.015044460073113441,370
185.0,0.012767013162374496,375
160.0,4.742649555206299,380
200.0,1.0178560018539429,385
200.0,0.08182927221059799,390
200.0,0.01739146187901497,395
200.0,0.012645881623029709,400
9.0,0.06882349401712418,405
39.0,0.09852810204029083,410
9.0,4.144737720489502,415
200.0,0.14002403616905212,420
200.0,1.0666762590408325,425
200.0,3.7281572818756104,430
200.0,0.03138595074415207,435
200.0,1.6102722883224487,440
200.0,0.039595987647771835,445
200.0,0.02767912670969963,450
183.0,0.012993409298360348,455
179.0,1.8382511138916016,460
200.0,0.01074942946434021,465
119.0,0.05010325461626053,470
125.0,0.04084702581167221,475
177.0,0.01885252445936203,480
167.0,0.5582287311553955,485
158.0,0.33888524770736694,490
200.0,0.03887341916561127,495
191.0,0.5189582705497742,500
200.0,4.817852973937988,505
200.0,0.011416086927056313,510
148.0,0.029537221416831017,515
140.0,0.018659140914678574,520
128.0,6.135469913482666,525
140.0,0.017004529014229774,530
141.0,0.07908019423484802,535
154.0,0.014127008616924286,540
136.0,0.11874083429574966,545
143.0,0.05152087286114693,550
200.0,0.010304479859769344,555
200.0,0.7502464056015015,560
157.0,0.06722593307495117,565
200.0,0.018013518303632736,570
200.0,5.614536762237549,575
200.0,0.035216063261032104,580
200.0,0.01069585606455803,585
200.0,0.04107843339443207,590
156.0,6.011622428894043,595


================================================
FILE: history/dueling.csv
================================================
Episode_reward,Loss,episode
30.0,inf,0
12.0,0.47862666845321655,5
10.0,0.5809901356697083,10
8.0,1.5168644189834595,15
10.0,6.394465446472168,20
11.0,5.444173336029053,25
11.0,6.27878999710083,30
11.0,6.766901969909668,35
10.0,0.267914354801178,40
27.0,0.7681541442871094,45
12.0,1.126452922821045,50
21.0,2.1125664710998535,55
13.0,1.1672120094299316,60
12.0,0.19179925322532654,65
28.0,1.5655790567398071,70
37.0,3.6122264862060547,75
25.0,0.4315088093280792,80
44.0,3.3004751205444336,85
54.0,3.665844440460205,90
43.0,0.26683512330055237,95
36.0,5.318463325500488,100
103.0,3.0524964332580566,105
50.0,1.7247264385223389,110
22.0,9.666183471679688,115
67.0,1.6773548126220703,120
141.0,0.22905239462852478,125
83.0,0.1352100670337677,130
98.0,0.08889307826757431,135
176.0,0.026787875220179558,140
150.0,0.06743741035461426,145
200.0,0.04945575073361397,150
200.0,0.026455968618392944,155
200.0,4.038379192352295,160
200.0,0.051251500844955444,165
200.0,0.018056631088256836,170
200.0,6.713091850280762,175
200.0,0.023181792348623276,180
200.0,4.3332624435424805,185
166.0,0.02410086989402771,190
200.0,0.11829394102096558,195
200.0,0.02812301553785801,200
96.0,2.3146820068359375,205
87.0,0.07561161369085312,210
121.0,0.11904805898666382,215
182.0,3.8535306453704834,220
159.0,0.8626901507377625,225
160.0,0.06850502640008926,230
194.0,0.04625943303108215,235
200.0,0.06545911729335785,240
200.0,0.02540893666446209,245
145.0,0.04876522347331047,250
200.0,0.0599154457449913,255
35.0,0.05168953537940979,260
200.0,0.04558023437857628,265
200.0,0.029182441532611847,270
200.0,2.2100436687469482,275
200.0,0.051673658192157745,280
200.0,4.435661792755127,285
200.0,0.043758101761341095,290
200.0,0.03457247465848923,295
200.0,0.019870392978191376,300
200.0,0.019909145310521126,305
9.0,0.1150365024805069,310
200.0,0.07036980986595154,315
71.0,0.05771002918481827,320
200.0,0.09849052131175995,325
200.0,0.045551616698503494,330
137.0,0.06604130566120148,335
11.0,0.09229154139757156,340
200.0,3.8334453105926514,345
200.0,0.02100004069507122,350
171.0,0.02557738684117794,355
200.0,5.395693302154541,360
200.0,0.021021392196416855,365
200.0,4.131258010864258,370
200.0,0.03307604417204857,375
172.0,0.059052661061286926,380
200.0,0.04051003232598305,385
200.0,0.018864136189222336,390
200.0,0.05375618487596512,395
39.0,0.0699274092912674,400
200.0,1.667770266532898,405
200.0,0.09974359720945358,410
155.0,4.035706520080566,415
157.0,0.07855170220136642,420
200.0,0.028804700821638107,425
162.0,1.9383234977722168,430
155.0,0.02189595438539982,435
200.0,0.014903474599123001,440
200.0,0.07733364403247833,445
187.0,0.022851835936307907,450
178.0,0.018380625173449516,455
131.0,0.04312446713447571,460
11.0,0.017067477107048035,465
200.0,0.07996176183223724,470
200.0,0.6626131534576416,475
200.0,0.019013769924640656,480
126.0,0.011018311604857445,485
185.0,0.0681515485048294,490
170.0,0.022165827453136444,495
200.0,0.025683466345071793,500
141.0,1.391233205795288,505
127.0,0.07900568842887878,510
200.0,0.13426055014133453,515
200.0,0.04910080507397652,520
200.0,5.457238674163818,525
116.0,0.046407174319028854,530
125.0,0.05579424649477005,535
176.0,0.07973217219114304,540
199.0,0.037510018795728683,545
142.0,0.04629438742995262,550
12.0,0.15213751792907715,555
10.0,0.025547455996274948,560
35.0,0.16393166780471802,565
196.0,0.10191859304904938,570
187.0,0.060145795345306396,575
188.0,0.028432875871658325,580
181.0,1.3723012208938599,585
200.0,0.035072945058345795,590
140.0,4.833233833312988,595


================================================
FILE: history/ndqn.csv
================================================
Episode_reward,Loss,episode
12.0,inf,0
39.0,0.5968765616416931,5
10.0,0.16437163949012756,10
8.0,0.8609060645103455,15
10.0,4.647477149963379,20
15.0,0.37436389923095703,25
18.0,1.042937159538269,30
32.0,0.25837475061416626,35
16.0,1.1858779191970825,40
29.0,0.13475389778614044,45
36.0,0.8968307375907898,50
75.0,0.09517502039670944,55
66.0,5.1964287757873535,60
169.0,0.8814995288848877,65
200.0,0.04609198123216629,70
200.0,0.07762521505355835,75
98.0,1.7214298248291016,80
66.0,0.02651367150247097,85
48.0,2.0342352390289307,90
89.0,0.04153415188193321,95
65.0,0.013355078175663948,100
90.0,0.026795051991939545,105
72.0,0.021573634818196297,110
106.0,0.02679624781012535,115
110.0,0.010724630206823349,120
200.0,0.9347834587097168,125
200.0,2.6067371368408203,130
200.0,0.03422386199235916,135
173.0,0.023121735081076622,140
74.0,0.03360900655388832,145
107.0,0.0349336713552475,150
105.0,3.056359052658081,155
89.0,1.5287914276123047,160
176.0,0.04428096488118172,165
156.0,0.020566236227750778,170
200.0,0.009313585236668587,175
200.0,0.011389960534870625,180
200.0,0.03430327773094177,185
200.0,0.03263617306947708,190
200.0,0.01683700643479824,195
200.0,0.022440128028392792,200
200.0,0.12438426911830902,205
199.0,0.02578575909137726,210
200.0,0.011934514157474041,215
200.0,0.016942474991083145,220
97.0,0.01568945124745369,225
114.0,0.12535057961940765,230
94.0,0.03555770218372345,235
200.0,0.04525809735059738,240
200.0,0.017689408734440804,245
200.0,0.0067247869446873665,250
200.0,0.07176659256219864,255
200.0,0.08657388389110565,260
141.0,0.033294644206762314,265
121.0,0.028403853997588158,270
200.0,2.290412664413452,275
200.0,0.02531827799975872,280
200.0,5.504024505615234,285
178.0,0.08186638355255127,290
200.0,0.08797675371170044,295
200.0,0.020431581884622574,300
132.0,5.576883316040039,305
200.0,4.848743438720703,310
200.0,0.016923055052757263,315
200.0,0.02683131955564022,320
124.0,0.015678368508815765,325
177.0,0.03775625675916672,330
137.0,0.05068105831742287,335
140.0,0.40797001123428345,340
166.0,0.02414533495903015,345
155.0,0.01930900663137436,350
164.0,0.011196689680218697,355
176.0,0.004606300499290228,360
167.0,0.0069297803565859795,365
152.0,0.003465760499238968,370
133.0,0.004011339973658323,375
178.0,0.014116533100605011,380
167.0,0.035929497331380844,385
188.0,0.056661978363990784,390
200.0,0.024751631543040276,395
200.0,4.973880290985107,400
168.0,0.09016193449497223,405
131.0,0.05364896357059479,410
143.0,0.03196289390325546,415
175.0,0.010464230552315712,420
137.0,0.043501902371644974,425
155.0,0.037118226289749146,430
124.0,0.013473874889314175,435
145.0,0.03192755952477455,440
200.0,0.02426156774163246,445
122.0,0.026452424004673958,450
162.0,3.928562641143799,455
131.0,0.029229773208498955,460
181.0,0.017442982643842697,465
186.0,0.007039761170744896,470
154.0,0.005024969577789307,475
200.0,0.016365719959139824,480
200.0,5.1473846435546875,485
12.0,0.027667846530675888,490
200.0,1.7594248056411743,495
200.0,0.08335447311401367,500
200.0,0.03457290679216385,505
200.0,0.011456770822405815,510
194.0,0.014004027470946312,515
174.0,0.031052935868501663,520
185.0,0.016657808795571327,525
200.0,0.018321434035897255,530
178.0,0.025208085775375366,535
200.0,0.009419861249625683,540
149.0,0.007532942574471235,545
149.0,0.01075670588761568,550
149.0,0.01898787170648575,555
127.0,0.013626886531710625,560
160.0,1.0664030313491821,565
200.0,0.006966365966945887,570
188.0,0.01090502180159092,575
110.0,0.009709188714623451,580
200.0,0.007233826443552971,585
200.0,5.274221897125244,590
200.0,0.058526113629341125,595


================================================
FILE: history/pg.csv
================================================
Batch_reward,Episode_reward,Loss,episode
126.0,25.2,-0.323914110660553,5
170.0,34.0,-0.3671955466270447,10
95.0,19.0,-0.31113868951797485,15
79.0,15.8,-0.31862735748291016,20
83.0,16.6,-0.31243768334388733,25
74.0,14.8,-0.2790282964706421,30
76.0,15.2,-0.30600470304489136,35
69.0,13.8,-0.2797362208366394,40
124.0,24.8,-0.31787288188934326,45
94.0,18.8,-0.32114318013191223,50
142.0,28.4,-0.3324996829032898,55
110.0,22.0,-0.3379059135913849,60
72.0,14.4,-0.31070083379745483,65
160.0,32.0,-0.36996564269065857,70
72.0,14.4,-0.3219853341579437,75
80.0,16.0,-0.3177907168865204,80
91.0,18.2,-0.31226593255996704,85
115.0,23.0,-0.3390122056007385,90
113.0,22.6,-0.32164397835731506,95
178.0,35.6,-0.35060709714889526,100
80.0,16.0,-0.3213381767272949,105
116.0,23.2,-0.3130705952644348,110
99.0,19.8,-0.3158109486103058,115
102.0,20.4,-0.32293465733528137,120
93.0,18.6,-0.30985644459724426,125
117.0,23.4,-0.33105579018592834,130
115.0,23.0,-0.3251442313194275,135
113.0,22.6,-0.356834352016449,140
126.0,25.2,-0.32076460123062134,145
100.0,20.0,-0.3276306986808777,150
89.0,17.8,-0.32530131936073303,155
82.0,16.4,-0.3186661899089813,160
128.0,25.6,-0.33558765053749084,165
101.0,20.2,-0.31853118538856506,170
108.0,21.6,-0.33060187101364136,175
120.0,24.0,-0.32635703682899475,180
154.0,30.8,-0.3299770951271057,185
116.0,23.2,-0.3230454623699188,190
197.0,39.4,-0.3595934212207794,195
93.0,18.6,-0.3127687871456146,200
133.0,26.6,-0.3340115547180176,205
125.0,25.0,-0.32840797305107117,210
99.0,19.8,-0.32053759694099426,215
139.0,27.8,-0.3281051516532898,220
141.0,28.2,-0.32602617144584656,225
145.0,29.0,-0.33977046608924866,230
96.0,19.2,-0.3226241171360016,235
114.0,22.8,-0.33663344383239746,240
111.0,22.2,-0.3363283574581146,245
162.0,32.4,-0.3411697745323181,250
128.0,25.6,-0.3296198844909668,255
91.0,18.2,-0.32588136196136475,260
111.0,22.2,-0.3307957649230957,265
107.0,21.4,-0.3256392478942871,270
129.0,25.8,-0.33310621976852417,275
66.0,13.2,-0.3237151503562927,280
121.0,24.2,-0.3433718979358673,285
75.0,15.0,-0.3230298161506653,290
117.0,23.4,-0.3261656165122986,295
176.0,35.2,-0.3627626895904541,300
121.0,24.2,-0.3326563835144043,305
96.0,19.2,-0.32595303654670715,310
98.0,19.6,-0.3163517415523529,315
83.0,16.6,-0.3248583674430847,320
123.0,24.6,-0.3384328782558441,325
84.0,16.8,-0.3206236958503723,330
122.0,24.4,-0.3618758022785187,335
71.0,14.2,-0.32045409083366394,340
102.0,20.4,-0.3334352970123291,345
78.0,15.6,-0.3175198435783386,350
154.0,30.8,-0.370138555765152,355
107.0,21.4,-0.3431130051612854,360
102.0,20.4,-0.3242567479610443,365
91.0,18.2,-0.32735154032707214,370
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================================================
FILE: history/ppo1.csv
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FILE: history/ppo2.csv
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