while True:
# fresh env
env.render()
# RL take action and get next observation and reward
observation_, reward, done = env.step(action)
# RL choose action based on next observation
action_ = RL.choose_action(str(observation_)) #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_ #状态为下一个状态
action = action_ #动作为下一个动作（这个很重要，表示他一个亲历亲为的on-policy）
# 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 = SarsaTable(actions=list(range(env.n_actions))) #给定一个sarsa算法
env.after(100, update)
env.mainloop()
# break while loop when end of this episode
if done:
print('Episode:', episode + 1, ' Reward: %i' % int(rewards))
print('Policy:', policy)
break
rewards_20.append(rewards)
policy_20.append(policy)
print('Best Reward:', np.max(rewards_20))
print('Best Policy:', policy_20[np.argmax(rewards_20)])
x = list(range(len(rewards_20)))
plt.plot(x, rewards_20)
#plt.title(f'Sarsa Result action_space: {action_space} learn_rate: {learning_rate} reward_decay: {reward_decay} e_greedy: {e_greedy}')
plt.title('Sarsa Result')
plt.xlabel('Episodes')
plt.ylabel('Rewards')
plt.show()
if __name__ == "__main__":
envSeqDec = ChallengeSeqDecEnvironment(experimentCount=20000)
action_space = [1, 2, 3,
4] # Using two actions can get steady 490 - 500 result.
RL = SarsaTable(actions=action_space,
learning_rate=0.05,
reward_decay=0.5,
e_greedy=0.9)
generate()
# 探索者在环境中实施这个 action, 并得到环境返回的下一个 state 观测值, reward 和 done (是否是掉下地狱或者升上天堂)
observation_, reward, done = env.step(action)
# # 根据下一个 state (obervation_) 选取下一个 action_
action_ = RL.choose_action(str(observation_))
# RL 从这个序列 (state, action, reward, state_) 中学习, # # 多了一个action_参数
RL.learn(str(observation), action, reward, str(observation_),
action_)
observation = observation_ # 将下一个 state 的值传到下一次循环
# # 下一个action_的值,变成action
action = action_
if done: # 如果掉下地狱或者升上天堂, 这回合就结束了
break
print('game over') # 结束游戏并关闭窗口
env.destroy()
if __name__ == "__main__":
# 定义环境 env 和 RL 方式
env = Maze()
RL = SarsaTable(actions=list(range(env.n_actions))) # # 创建SaraTable类
# 开始可视化环境 env
env.after(100, update)
env.mainloop()
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 SarsaTable
if __name__ == '__main__':
env = Maze()
RL = SarsaTable(actions=env.action_space)
def update():
log = []
for episode in range(100):
s = env.reset()
a = RL.choose_action(str(s))
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.learn(str(s), a, r, str(s_), a_)
from maze_env import Maze
from RL_brain import SarsaTable
def update():
for episode in range(100):
observation = env.reset()
action = RL.choose_action(str(observation))
while True:
env.render()
observation_,reward,done = env.step(action)
action_ = RL.choose_action(str(observation_))
RL.learn(str(observation),action,reward,str(observation_),action_)
observation = observation_
action = action_
if done:
break
print('game over')
env.destroy()
if __name__ == '__main__':
env = Maze()
RL = SarsaTable(action_space=list(range(env.n_actions)))
env.after(100,update)
env.mainloop()
