Gradiente de estrategia de aprendizaje por refuerzo

import gym
import tensorflow as tf
import numpy as np
import random
from collections import deque

# Hyper Parameters
GAMMA = 0.95 # discount factor
LEARNING_RATE=0.01

class Policy_Gradient():
    def __init__(self, env):
        # init some parameters
        self.time_step = 0
        self.state_dim = env.observation_space.shape[0] # 4
        self.action_dim = env.action_space.n # 2
        # print(self.state_dim, self.action_dim)

        self.ep_obs, self.ep_as, self.ep_rs = [], [], []

        self.create_softmax_network()

        # Init session
        self.session = tf.InteractiveSession()
        self.session.run(tf.global_variables_initializer())

    def create_softmax_network(self):
        # network weights
        W1 = self.weight_variable([self.state_dim, 20])
        b1 = self.bias_variable([20])
        W2 = self.weight_variable([20, self.action_dim])
        b2 = self.bias_variable([self.action_dim])

        # input layer
        self.state_input = tf.placeholder("float", [None, self.state_dim]) # input
        self.tf_acts = tf.placeholder(tf.int32, [None, ], name="actions_num") # action

        self.tf_vt = tf.placeholder(tf.float32, [None, ], name="actions_value") # discount_reward

        # hidden layers
        h_layer = tf.nn.relu(tf.matmul(self.state_input, W1) + b1)
        # softmax layer
        self.softmax_input = tf.matmul(h_layer, W2) + b2
        print('self.softmax_input:', self.softmax_input)
        #softmax output

        self.all_act_prob = tf.nn.softmax(self.softmax_input, name='act_prob')
        print("self.all_act_prob:", self.all_act_prob)
        self.neg_log_prob = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.softmax_input,
                                                                      labels=self.tf_acts)# action
        self.loss = tf.reduce_mean(self.neg_log_prob * self.tf_vt)  # reward guided loss

        self.train_op = tf.train.AdamOptimizer(LEARNING_RATE).minimize(self.loss)

    def weight_variable(self, shape):
        initial = tf.truncated_normal(shape)
        return tf.Variable(initial)

    def bias_variable(self, shape):
        initial = tf.constant(0.01, shape=shape)
        return tf.Variable(initial)

    def choose_action(self, observation):
        '''

        :param observation:
        :return:
        '''
        # 当前策略采样。
        prob_weights = self.session.run(self.all_act_prob, feed_dict={self.state_input: observation[np.newaxis, :]})
        # e-greedy action
        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):

        discounted_ep_rs = np.zeros_like(self.ep_rs)
        running_add = 0
        # 处理收集到的reward
        for t in reversed(range(0, len(self.ep_rs))):
            running_add = running_add * GAMMA + self.ep_rs[t] # 蒙特卡洛，获取值函数
            discounted_ep_rs[t] = running_add

        discounted_ep_rs -= np.mean(discounted_ep_rs)
        discounted_ep_rs /= np.std(discounted_ep_rs)

        # train on episode
        a, b, c = self.session.run([self.softmax_input, self.all_act_prob ,self.train_op], feed_dict={
             self.state_input: np.vstack(self.ep_obs),
             self.tf_acts: np.array(self.ep_as),
             self.tf_vt: discounted_ep_rs, #
        })

        print('a:',a, 'b:',b)
        self.ep_obs, self.ep_as, self.ep_rs = [], [], []    # empty episode data
# Hyper Parameters
ENV_NAME = 'CartPole-v0'
EPISODE = 3000 # Episode limitation
STEP = 3000 # Step limitation in an episode
TEST = 10 # The number of experiment test every 100 episode

def main():
  # initialize OpenAI Gym env and dqn agent
  env = gym.make(ENV_NAME)

  agent = Policy_Gradient(env)

  for episode in range(EPISODE):
    # initialize task
    state = env.reset()
    # Train
    for step in range(STEP):
      action = agent.choose_action(state) # e-greedy action for train

      next_state,reward,done,_ = env.step(action)
      agent.store_transition(state, action, reward)
      state = next_state
      if done:
        #print("stick for ",step, " steps")
        agent.learn()
        break

    # Test every 100 episodes
    if episode % 100 == 0:
      total_reward = 0
      for i in range(TEST):
        state = env.reset()
        for j in range(STEP):
          # env.render()
          action = agent.choose_action(state) # direct action for test

          state,reward,done,_ = env.step(action)
          total_reward += reward
          if done:
            break
      ave_reward = total_reward/TEST
      print ('episode: ',episode,'Evaluation Average Reward:',ave_reward)

if __name__ == '__main__':
  main()