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