Policy_Gradient.zip

""" This part of code is the reinforcement learning brain, which is a brain of the agent. All decisions are made in here. Policy Gradient, Reinforcement Learning. View more on my tutorial page: https://morvanzhou.github.io/tutorials/ Using: Tensorflow: 1.0 gym: 0.8.0 """ import numpy as np import tensorflow as tf # reproducible np.random.seed(1) tf.set_random_seed(1) class PolicyGradient: def __init__( self, n_actions, n_features, learning_rate=0.01, reward_decay=0.95, output_graph=False, ): self.n_actions = n_actions self.n_features = n_features self.lr = learning_rate self.gamma = reward_decay self.ep_obs, self.ep_as, self.ep_rs = [], [], [] self._build_net() self.sess = tf.Session() if output_graph: # $ tensorboard --logdir=logs # http://0.0.0.0:6006/ # tf.train.SummaryWriter soon be deprecated, use following tf.summary.FileWriter("logs/", self.sess.graph) self.sess.run(tf.global_variables_initializer()) def _build_net(self): with tf.name_scope('inputs'): self.tf_obs = tf.placeholder(tf.float32, [None, self.n_features], name="observations") self.tf_acts = tf.placeholder(tf.int32, [None, ], name="actions_num") self.tf_vt = tf.placeholder(tf.float32, [None, ], name="actions_value") # fc1 layer = tf.layers.dense( inputs=self.tf_obs, units=10, activation=tf.nn.tanh, # tanh activation kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.3), bias_initializer=tf.constant_initializer(0.1), name='fc1' ) # fc2 all_act = tf.layers.dense( inputs=layer, units=self.n_actions, activation=None, kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.3), bias_initializer=tf.constant_initializer(0.1), name='fc2' ) self.all_act_prob = tf.nn.softmax(all_act, name='act_prob') # use softmax to convert to probability with tf.name_scope('loss'): # to maximize total reward (log_p * R) is to minimize -(log_p * R), and the tf only have minimize(loss) neg_log_prob = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=all_act, labels=self.tf_acts) # this is negative log of chosen action # or in this way: # neg_log_prob = tf.reduce_sum(-tf.log(self.all_act_prob)*tf.one_hot(self.tf_acts, self.n_actions), axis=1) loss = tf.reduce_mean(neg_log_prob * self.tf_vt) # reward guided loss with tf.name_scope('train'): self.train_op = tf.train.AdamOptimizer(self.lr).minimize(loss) def choose_action(self, observation): prob_weights = self.sess.run(self.all_act_prob, feed_dict={self.tf_obs: observation[np.newaxis, :]}) action = np.random.choice(range(prob_weights.shape[1]), p=prob_weights.ravel()) # select action w.r.t the actions prob return action def store_transition(self, s, a, r): self.ep_obs.append(s) self.ep_as.append(a) self.ep_rs.append(r) def learn(self): # discount and normalize episode reward discounted_ep_rs_norm = self._discount_and_norm_rewards() # train on episode self.sess.run(self.train_op, feed_dict={ self.tf_obs: np.vstack(self.ep_obs), # shape=[None, n_obs] self.tf_acts: np.array(self.ep_as), # shape=[None, ] self.tf_vt: discounted_ep_rs_norm, # shape=[None, ] }) self.ep_obs, self.ep_as, self.ep_rs = [], [], [] # empty episode data return discounted_ep_rs_norm def _discount_and_norm_rewards(self): # discount episode rewards discounted_ep_rs = np.zeros_like(self.ep_rs) running_add = 0 for t in reversed(range(0, len(self.ep_rs))): running_add = running_add * self.gamma + self.ep_rs[t] discounted_ep_rs[t] = running_add # normalize episode rewards discounted_ep_rs -= np.mean(discounted_ep_rs) discounted_ep_rs /= np.std(discounted_ep_rs) return discounted_ep_rs