Source code for tensorlayer.rein

#! /usr/bin/python
# -*- coding: utf8 -*-

import tensorflow as tf
import numpy as np
from six.moves import xrange

[docs]def discount_episode_rewards(rewards=[], gamma=0.99, mode=0):
""" Take 1D float array of rewards and compute discounted rewards for an
episode. When encount a non-zero value, consider as the end a of an episode.

Parameters
----------
rewards : numpy list
a list of rewards
gamma : float
discounted factor
mode : int
if mode == 0, reset the discount process when encount a non-zero reward (Ping-pong game).
if mode == 1, would not reset the discount process.

Examples
----------
>>> rewards = np.asarray([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1])
>>> gamma = 0.9
>>> discount_rewards = tl.rein.discount_episode_rewards(rewards, gamma)
>>> print(discount_rewards)
... [ 0.72899997  0.81        0.89999998  1.          0.72899997  0.81
... 0.89999998  1.          0.72899997  0.81        0.89999998  1.        ]
>>> discount_rewards = tl.rein.discount_episode_rewards(rewards, gamma, mode=1)
>>> print(discount_rewards)
... [ 1.52110755  1.69011939  1.87791049  2.08656716  1.20729685  1.34144104
... 1.49048996  1.65610003  0.72899997  0.81        0.89999998  1.        ]
"""
discounted_r = np.zeros_like(rewards, dtype=np.float32)
for t in reversed(xrange(0, rewards.size)):
if mode == 0:
if rewards[t] != 0: running_add = 0

return discounted_r

[docs]def cross_entropy_reward_loss(logits, actions, rewards, name=None):
""" Calculate the loss for Policy Gradient Network.

Parameters
----------
logits : tensor
The network outputs without softmax. This function implements softmax
inside.
actions : tensor/ placeholder
The agent actions.
rewards : tensor/ placeholder
The rewards.

Examples
----------
>>> states_batch_pl = tf.placeholder(tf.float32, shape=[None, D])
>>> network = InputLayer(states_batch_pl, name='input')
>>> network = DenseLayer(network, n_units=H, act=tf.nn.relu, name='relu1')
>>> network = DenseLayer(network, n_units=3, name='out')
>>> probs = network.outputs
>>> sampling_prob = tf.nn.softmax(probs)
>>> actions_batch_pl = tf.placeholder(tf.int32, shape=[None])
>>> discount_rewards_batch_pl = tf.placeholder(tf.float32, shape=[None])
>>> loss = tl.rein.cross_entropy_reward_loss(probs, actions_batch_pl, discount_rewards_batch_pl)
>>> train_op = tf.train.RMSPropOptimizer(learning_rate, decay_rate).minimize(loss)
"""

try: # TF 1.0+
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=actions, logits=logits, name=name)
except:
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, targets=actions)
# cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, actions)

try: ## TF1.0+
loss = tf.reduce_sum(tf.multiply(cross_entropy, rewards))
except: ## TF0.12
loss = tf.reduce_sum(tf.mul(cross_entropy, rewards))   # element-wise mul
return loss

[docs]def log_weight(probs, weights, name='log_weight'):
"""Log weight.

Parameters
-----------
probs : tensor
If it is a network output, usually we should scale it to [0, 1] via softmax.
weights : tensor
"""
with tf.variable_scope(name):
exp_v = tf.reduce_mean(tf.log(probs) * weights)
return exp_v

[docs]def choice_action_by_probs(probs=[0.5, 0.5], action_list=None):
"""Choice and return an an action by given the action probability distribution.

Parameters
------------
probs : a list of float.
The probability distribution of all actions.
action_list : None or a list of action in integer, string or others.
If None, returns an integer range between 0 and len(probs)-1.

Examples
----------
>>> for _ in range(5):
>>>     a = choice_action_by_probs([0.2, 0.4, 0.4])
>>>     print(a)
... 0
... 1
... 1
... 2
... 1
>>> for _ in range(3):
>>>     a = choice_action_by_probs([0.5, 0.5], ['a', 'b'])
>>>     print(a)
... a
... b
... b
"""
if action_list is None:
n_action = len(probs)
action_list = np.arange(n_action)
else:
assert len(action_list) == len(probs), "Number of actions should equal to number of probabilities."
return np.random.choice(action_list, p=probs)