You will see the sarsa is more coward when punishment is close because it cares about all behaviours,
while q learning is more brave because it only cares about maximum behaviour.
"""
import sys
import os
sys.path.append(os.getcwd())
import pandas as pd
import time
from maze_env import Maze
from RL_brain import SarsaLambdaTable
if __name__ == '__main__':
env = Maze()
RL = SarsaLambdaTable(actions=env.action_space)
def update():
log = []
for episode in range(100):
s = env.reset()
a = RL.choose_action(str(s))
RL.eligibility_trace *= 0 # 清空路径
step_count = 0
done = False
r = 0
while not done:
env.render()
s_, r, done = env.step(a)
a_ = RL.choose_action(str(s_))
# RL take action and get next observation and reward
observation_, reward, done = env.step(
action) # 获取当前动作后的环境和reward情况
# RL choose action based on next observation
action_ = RL.choose_action(str(observation_)) # 根据环境选择下一个动作
# RL learn from this transition (s, a, r, s, a) ==> Sarsa
RL.learn(str(observation), action, reward, str(observation_),
action_)
# swap observation and action
observation = observation_
action = action_
# break while loop when end of this episode
if done:
break
# end of game
print('game over')
env.destroy()
if __name__ == "__main__":
env = Maze()
RL = SarsaLambdaTable(actions=list(range(env.n_actions)))
env.after(100, update)
env.mainloop()
# RL choose action based on next observation
action_ = RL.choose_action(str(observation_))
# RL learn from this transition (s, a, r, s, a) ==> Sarsa
RL.learn(str(observation), action, reward, str(observation_),
action_)
# swap observation and action
observation = observation_
action = action_
# break while loop when end of this episode
if done:
#保存q_table时使用
if RL.read_save:
RL.save_q_table()
break
# end of game
print('game over')
print("Q_table:\n", RL.q_table)
env.destroy()
if __name__ == "__main__":
env = Maze()
RL = SarsaLambdaTable(actions=list(range(env.n_actions)),
read_save=read_save)
env.after(100, update)
env.mainloop()
