SYMBOL INDEX (231 symbols across 24 files)

FILE: hw1/BehavioralCloning.py
  class Config (line 10) | class Config(object):
  class NN (line 24) | class NN(object):
    method add_placeholders (line 25) | def add_placeholders(self):
    method create_feed_dict (line 31) | def create_feed_dict(self, inputs_batch, labels_batch=None, dropout=1,...
    method add_prediction_op (line 40) | def add_prediction_op(self):
    method add_loss_op (line 57) | def add_loss_op(self, pred):
    method add_training_op (line 62) | def add_training_op(self, loss):
    method train_on_batch (line 69) | def train_on_batch(self, sess, inputs_batch, labels_batch, merged, tra...
    method __init__ (line 75) | def __init__(self, config):
    method fit (line 79) | def fit(self, sess, train_x, train_y):
    method build (line 82) | def build(self):
    method get_pred (line 92) | def get_pred(self, sess, inputs_batch):
  function load (line 97) | def load(path):
  function main (line 105) | def main():

FILE: hw1/DAgger.py
  class Config (line 11) | class Config(object):
  class NN (line 25) | class NN(object):
    method add_placeholders (line 26) | def add_placeholders(self):
    method create_feed_dict (line 32) | def create_feed_dict(self, inputs_batch, labels_batch=None, dropout=1,...
    method add_prediction_op (line 41) | def add_prediction_op(self):
    method add_loss_op (line 57) | def add_loss_op(self, pred):
    method add_training_op (line 62) | def add_training_op(self, loss):
    method train_on_batch (line 69) | def train_on_batch(self, sess, inputs_batch, labels_batch, merged, tra...
    method __init__ (line 75) | def __init__(self, config):
    method fit (line 79) | def fit(self, sess, train_x, train_y):
    method build (line 82) | def build(self):
    method get_pred (line 92) | def get_pred(self, sess, inputs_batch):
  function load (line 97) | def load(path):
  function run_env (line 104) | def run_env(env, nn,session):
  function shuffle (line 122) | def shuffle(X_train, y_train):
  function main (line 130) | def main():

FILE: hw1/load_policy.py
  function load_policy (line 3) | def load_policy(filename):

FILE: hw1/logz.py
  function colorize (line 34) | def colorize(string, color, bold=False, highlight=False):
  class G (line 42) | class G:
  function configure_output_dir (line 49) | def configure_output_dir(d=None):
  function log_tabular (line 62) | def log_tabular(key, val):
  function save_params (line 74) | def save_params(params):
  function pickle_tf_vars (line 78) | def pickle_tf_vars():
  function dump_tabular (line 88) | def dump_tabular():

FILE: hw1/plot.py
  function plot_data (line 51) | def plot_data(data, value="AverageReturn"):
  function get_datasets (line 60) | def get_datasets(fpath, condition=None):
  function main (line 91) | def main():

FILE: hw1/run_expert.py
  function main (line 21) | def main():

FILE: hw1/tf_util.py
  function sum (line 18) | def sum(x, axis=None, keepdims=False):
  function mean (line 20) | def mean(x, axis=None, keepdims=False):
  function var (line 22) | def var(x, axis=None, keepdims=False):
  function std (line 25) | def std(x, axis=None, keepdims=False):
  function max (line 27) | def max(x, axis=None, keepdims=False):
  function min (line 29) | def min(x, axis=None, keepdims=False):
  function concatenate (line 31) | def concatenate(arrs, axis=0):
  function argmax (line 33) | def argmax(x, axis=None):
  function switch (line 36) | def switch(condition, then_expression, else_expression):
  function l2loss (line 55) | def l2loss(params):
  function lrelu (line 60) | def lrelu(x, leak=0.2):
  function categorical_sample_logits (line 64) | def categorical_sample_logits(X):
  function get_session (line 73) | def get_session():
  function single_threaded_session (line 76) | def single_threaded_session():
  function make_session (line 82) | def make_session(num_cpu):
  function initialize (line 90) | def initialize():
  function eval (line 96) | def eval(expr, feed_dict=None):
  function set_value (line 100) | def set_value(v, val):
  function load_state (line 103) | def load_state(fname):
  function save_state (line 107) | def save_state(fname):
  function normc_initializer (line 117) | def normc_initializer(std=1.0):
  function conv2d (line 125) | def conv2d(x, num_filters, name, filter_size=(3, 3), stride=(1, 1), pad=...
  function dense (line 155) | def dense(x, size, name, weight_init=None, bias=True):
  function wndense (line 164) | def wndense(x, size, name, init_scale=1.0):
  function densenobias (line 175) | def densenobias(x, size, name, weight_init=None):
  function dropout (line 178) | def dropout(x, pkeep, phase=None, mask=None):
  function batchnorm (line 185) | def batchnorm(x, name, phase, updates, gamma=0.96):
  function function (line 213) | def function(inputs, outputs, updates=None, givens=None):
  class _Function (line 223) | class _Function(object):
    method __init__ (line 224) | def __init__(self, inputs, outputs, updates, givens, check_nan=False):
    method __call__ (line 232) | def __call__(self, *inputvals):
  function mem_friendly_function (line 242) | def mem_friendly_function(nondata_inputs, data_inputs, outputs, batch_si...
  class _MemFriendlyFunction (line 249) | class _MemFriendlyFunction(object):
    method __init__ (line 250) | def __init__(self, nondata_inputs, data_inputs, outputs, batch_size):
    method __call__ (line 255) | def __call__(self, *inputvals):
  class Module (line 281) | class Module(object):
    method __init__ (line 282) | def __init__(self, name):
    method __call__ (line 287) | def __call__(self, *args):
    method _call (line 303) | def _call(self, *args):
    method trainable_variables (line 307) | def trainable_variables(self):
    method variables (line 312) | def variables(self):
  function module (line 317) | def module(name):
  function get_parents (line 333) | def get_parents(node):
  function topsorted (line 336) | def topsorted(outputs):
  function var_shape (line 377) | def var_shape(x):
  function numel (line 383) | def numel(x):
  function intprod (line 386) | def intprod(x):
  function flatgrad (line 389) | def flatgrad(loss, var_list):
  class SetFromFlat (line 394) | class SetFromFlat(object):
    method __init__ (line 395) | def __init__(self, var_list, dtype=tf.float32):
    method __call__ (line 408) | def __call__(self, theta):
  class GetFlat (line 411) | class GetFlat(object):
    method __init__ (line 412) | def __init__(self, var_list):
    method __call__ (line 414) | def __call__(self):
  function fancy_slice_2d (line 422) | def fancy_slice_2d(X, inds0, inds1):
  function scope_vars (line 435) | def scope_vars(scope, trainable_only):
  function lengths_to_mask (line 445) | def lengths_to_mask(lengths_b, max_length):
  function in_session (line 463) | def in_session(f):
  function get_placeholder (line 472) | def get_placeholder(name, dtype, shape):
  function get_placeholder_cached (line 482) | def get_placeholder_cached(name):
  function flattenallbut0 (line 485) | def flattenallbut0(x):
  function reset (line 488) | def reset():

FILE: hw2/logz.py
  function colorize (line 34) | def colorize(string, color, bold=False, highlight=False):
  class G (line 42) | class G:
  function configure_output_dir (line 49) | def configure_output_dir(d=None):
  function log_tabular (line 60) | def log_tabular(key, val):
  function save_params (line 72) | def save_params(params):
  function pickle_tf_vars (line 76) | def pickle_tf_vars():
  function dump_tabular (line 86) | def dump_tabular():

FILE: hw2/plot.py
  function plot_data (line 52) | def plot_data(data, value="AverageReturn"):
  function get_datasets (line 62) | def get_datasets(fpath, condition=None):
  function main (line 91) | def main():

FILE: hw2/train_pg.py
  function build_mlp (line 15) | def build_mlp(
  function pathlength (line 44) | def pathlength(path):
  function train_PG (line 53) | def train_PG(exp_name='',
  function main (line 434) | def main():

FILE: hw3/atari_wrappers.py
  class NoopResetEnv (line 8) | class NoopResetEnv(gym.Wrapper):
    method __init__ (line 9) | def __init__(self, env=None, noop_max=30):
    method _reset (line 17) | def _reset(self):
  class FireResetEnv (line 25) | class FireResetEnv(gym.Wrapper):
    method __init__ (line 26) | def __init__(self, env=None):
    method _reset (line 32) | def _reset(self):
  class EpisodicLifeEnv (line 38) | class EpisodicLifeEnv(gym.Wrapper):
    method __init__ (line 39) | def __init__(self, env=None):
    method _step (line 48) | def _step(self, action):
    method _reset (line 62) | def _reset(self):
  class MaxAndSkipEnv (line 77) | class MaxAndSkipEnv(gym.Wrapper):
    method __init__ (line 78) | def __init__(self, env=None, skip=4):
    method _step (line 85) | def _step(self, action):
    method _reset (line 99) | def _reset(self):
  function _process_frame84 (line 106) | def _process_frame84(frame):
  class ProcessFrame84 (line 114) | class ProcessFrame84(gym.Wrapper):
    method __init__ (line 115) | def __init__(self, env=None):
    method _step (line 119) | def _step(self, action):
    method _reset (line 123) | def _reset(self):
  class ClippedRewardsWrapper (line 126) | class ClippedRewardsWrapper(gym.Wrapper):
    method _step (line 127) | def _step(self, action):
  function wrap_deepmind_ram (line 131) | def wrap_deepmind_ram(env):
  function wrap_deepmind (line 140) | def wrap_deepmind(env):

FILE: hw3/dqn.py
  function learn (line 14) | def learn(env,

FILE: hw3/dqn_utils.py
  function huber_loss (line 8) | def huber_loss(x, delta=1.0):
  function sample_n_unique (line 16) | def sample_n_unique(sampling_f, n):
  class Schedule (line 27) | class Schedule(object):
    method value (line 28) | def value(self, t):
  class ConstantSchedule (line 32) | class ConstantSchedule(object):
    method __init__ (line 33) | def __init__(self, value):
    method value (line 42) | def value(self, t):
  function linear_interpolation (line 46) | def linear_interpolation(l, r, alpha):
  class PiecewiseSchedule (line 49) | class PiecewiseSchedule(object):
    method __init__ (line 50) | def __init__(self, endpoints, interpolation=linear_interpolation, outs...
    method value (line 74) | def value(self, t):
  class LinearSchedule (line 85) | class LinearSchedule(object):
    method __init__ (line 86) | def __init__(self, schedule_timesteps, final_p, initial_p=1.0):
    method value (line 104) | def value(self, t):
  function compute_exponential_averages (line 109) | def compute_exponential_averages(variables, decay):
  function minimize_and_clip (line 130) | def minimize_and_clip(optimizer, objective, var_list, clip_val=10):
  function initialize_interdependent_variables (line 141) | def initialize_interdependent_variables(session, vars_list, feed_dict):
  function get_wrapper_by_name (line 164) | def get_wrapper_by_name(env, classname):
  class ReplayBuffer (line 174) | class ReplayBuffer(object):
    method __init__ (line 175) | def __init__(self, size, frame_history_len):
    method can_sample (line 212) | def can_sample(self, batch_size):
    method _encode_sample (line 216) | def _encode_sample(self, idxes):
    method sample (line 226) | def sample(self, batch_size):
    method encode_recent_observation (line 263) | def encode_recent_observation(self):
    method _encode_observation (line 276) | def _encode_observation(self, idx):
    method store_frame (line 302) | def store_frame(self, frame):
    method store_effect (line 330) | def store_effect(self, idx, action, reward, done):

FILE: hw3/logz.py
  function colorize (line 34) | def colorize(string, color, bold=False, highlight=False):
  class G (line 42) | class G:
  function configure_output_dir (line 49) | def configure_output_dir(d=None):
  function log_tabular (line 60) | def log_tabular(key, val):
  function save_params (line 72) | def save_params(params):
  function pickle_tf_vars (line 76) | def pickle_tf_vars():
  function dump_tabular (line 86) | def dump_tabular():

FILE: hw3/plot.py
  function plot_data (line 51) | def plot_data(data, value="MeanReward"):
  function get_datasets (line 61) | def get_datasets(fpath, condition=None):
  function main (line 90) | def main():

FILE: hw3/run_dqn_atari.py
  function atari_model (line 18) | def atari_model(img_in, num_actions, scope, reuse=False):
  function atari_learn (line 34) | def atari_learn(env,
  function get_available_gpus (line 84) | def get_available_gpus():
  function set_global_seeds (line 89) | def set_global_seeds(i):
  function get_session (line 99) | def get_session():
  function get_env (line 108) | def get_env(task, seed):
  function main (line 122) | def main():

FILE: hw3/run_dqn_ram.py
  function atari_model (line 17) | def atari_model(ram_in, num_actions, scope, reuse=False):
  function atari_learn (line 29) | def atari_learn(env,
  function get_available_gpus (line 79) | def get_available_gpus():
  function set_global_seeds (line 84) | def set_global_seeds(i):
  function get_session (line 94) | def get_session():
  function get_env (line 103) | def get_env(seed):
  function main (line 115) | def main():

FILE: hw4/cheetah_env.py
  class HalfCheetahEnvNew (line 5) | class HalfCheetahEnvNew(mujoco_env.MujocoEnv, utils.EzPickle):
    method __init__ (line 6) | def __init__(self):
    method _step (line 10) | def _step(self, action):
    method _get_obs (line 21) | def _get_obs(self):
    method reset_model (line 29) | def reset_model(self):
    method viewer_setup (line 35) | def viewer_setup(self):

FILE: hw4/controllers.py
  class Controller (line 6) | class Controller():
    method __init__ (line 7) | def __init__(self):
    method get_action (line 11) | def get_action(self, state):
  class RandomController (line 15) | class RandomController(Controller):
    method __init__ (line 16) | def __init__(self, env):
    method get_action (line 20) | def get_action(self, state):
  class MPCcontroller (line 26) | class MPCcontroller(Controller):
    method __init__ (line 28) | def __init__(self,
    method get_action (line 41) | def get_action(self, state):

FILE: hw4/cost_functions.py
  function cheetah_cost_fn (line 9) | def cheetah_cost_fn(state, action, next_state):
  function trajectory_cost_fn (line 58) | def trajectory_cost_fn(cost_fn, states, actions, next_states):

FILE: hw4/dynamics.py
  function build_mlp (line 5) | def build_mlp(input_placeholder,
  class NNDynamicsModel (line 20) | class NNDynamicsModel():
    method __init__ (line 21) | def __init__(self,
    method fit (line 51) | def fit(self, data):
    method predict (line 82) | def predict(self, states, actions):

FILE: hw4/logz.py
  function colorize (line 34) | def colorize(string, color, bold=False, highlight=False):
  class G (line 42) | class G:
  function configure_output_dir (line 49) | def configure_output_dir(d=None):
  function log_tabular (line 62) | def log_tabular(key, val):
  function save_params (line 74) | def save_params(params):
  function pickle_tf_vars (line 78) | def pickle_tf_vars():
  function dump_tabular (line 88) | def dump_tabular():

FILE: hw4/main.py
  function sample (line 15) | def sample(env,
  function path_cost (line 51) | def path_cost(cost_fn, path):
  function compute_normalization (line 54) | def compute_normalization(data):
  function plot_comparison (line 76) | def plot_comparison(env, dyn_model):
  function train (line 84) | def train(env,
  function main (line 227) | def main():

FILE: hw4/plot.py
  function plot_data (line 51) | def plot_data(data, value="AverageReturn"):
  function get_datasets (line 60) | def get_datasets(fpath, condition=None):
  function main (line 89) | def main():