增强学习--Sarsa算法

Sarsa算法

实例代码

 import numpy as np
 import random
 from collections import defaultdict
 from environment import Env

 # SARSA agent learns every time step from the sample <s, a, r, s', a'>
 class SARSAgent:
     def __init__(self, actions):
         self.actions = actions
         self.learning_rate = 0.01
         self.discount_factor = 0.9
         self.epsilon = 0.1
         self.q_table = defaultdict(lambda: [0.0, 0.0, 0.0, 0.0])#动作值函数表,q表,要更新的表，不同于mc的更新v表

     # with sample <s, a, r, s', a'>, learns new q function
     def learn(self, state, action, reward, next_state, next_action):
         current_q = self.q_table[state][action]
         next_state_q = self.q_table[next_state][next_action]
         new_q = (current_q + self.learning_rate *
                 (reward + self.discount_factor * next_state_q - current_q))#q表更新公式
         self.q_table[state][action] = new_q

     # get action for the state according to the q function table
     # agent pick action of epsilon-greedy policy
     def get_action(self, state):#获取下一步动作
         #epsilon-greedy policy,exploration
         if np.random.rand() < self.epsilon:
             # take random action
             action = np.random.choice(self.actions)
         else:
             # take action according to the q function table
             state_action = self.q_table[state]
             action = self.arg_max(state_action)
         return action

     @staticmethod
     def arg_max(state_action):
         max_index_list = []
         max_value = state_action[0]
         for index, value in enumerate(state_action):
             if value > max_value:
                 max_index_list.clear()
                 max_value = value
                 max_index_list.append(index)
             elif value == max_value:
                 max_index_list.append(index)
         return random.choice(max_index_list)

 if __name__ == "__main__":
     env = Env()
     agent = SARSAgent(actions=list(range(env.n_actions)))

     for episode in range(1000):
         # reset environment and initialize state

         state = env.reset()
         # get action of state from agent
         action = agent.get_action(str(state))

         while True:
             env.render()

             # take action and proceed one step in the environment
             next_state, reward, done = env.step(action)
             next_action = agent.get_action(str(next_state))

             # with sample <s,a,r,s',a'>, agent learns new q function
             agent.learn(str(state), action, reward, str(next_state), next_action)

             state = next_state
             action = next_action

             # print q function of all states at screen
             env.print_value_all(agent.q_table)

             # if episode ends, then break
             if done:
                 break