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) running_add = 0 for t in reversed(xrange(0, rewards.size)): if mode == 0: if rewards[t] != 0: running_add = 0 running_add = running_add * gamma + rewards[t] discounted_r[t] = running_add 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)