CliffWalking

• 如下图所示，S是起点，C是障碍，G是目标
• agent从S开始走，目标是找到到G的最短路径
• 这里reward可以建模成-1，最终目标是让return最大，也就是路径最短
  

代码和解释

import gym
import numpy as np
import time# agent.py
class SarsaAgent(object):def __init__(self, obs_n, act_n, learning_rate=0.01, gamma=0.9, e_greed=0.1):self.act_n = act_n      # 动作维度，有几个动作可选self.lr = learning_rate # 学习率self.gamma = gamma      # reward的衰减率self.epsilon = e_greed  # 按一定概率随机选动作self.Q = np.zeros((obs_n, act_n))# 根据输入观察值，采样输出的动作值，带探索def sample(self, obs):if np.random.uniform(0, 1) < (1.0 - self.epsilon): #根据table的Q值选动作action = self.predict(obs)else:action = np.random.choice(self.act_n) #有一定概率随机探索选取一个动作return action# 根据输入观察值，预测输出的动作值def predict(self, obs):Q_list = self.Q[obs, :]maxQ = np.max(Q_list)action_list = np.where(Q_list == maxQ)[0]  # maxQ可能对应多个actionaction = np.random.choice(action_list)return action# 学习方法，也就是更新Q-table的方法def learn(self, obs, action, reward, next_obs, next_action, done):""" on-policyobs: 交互前的obs, s_taction: 本次交互选择的action, a_treward: 本次动作获得的奖励rnext_obs: 本次交互后的obs, s_t+1next_action: 根据当前Q表格, 针对next_obs会选择的动作, a_t+1done: episode是否结束"""predict_Q = self.Q[obs, action]if done:target_Q = reward # 没有下一个状态了else:target_Q = reward + self.gamma * self.Q[next_obs, next_action] # Sarsaself.Q[obs, action] += self.lr * (target_Q - predict_Q) # 修正q# 保存Q表格数据到文件def save(self):npy_file = './q_table.npy'np.save(npy_file, self.Q)print(npy_file + ' saved.')# 从文件中读取Q值到Q表格中def restore(self, npy_file='./q_table.npy'):self.Q = np.load(npy_file)print(npy_file + ' loaded.')def run_episode(env, agent, render=False):total_steps = 0 # 记录每个episode走了多少steptotal_reward = 0obs = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）action = agent.sample(obs) # 根据算法选择一个动作while True:next_obs, reward, done, _ = env.step(action) # 与环境进行一个交互next_action = agent.sample(next_obs) # 根据算法选择一个动作# 训练 Sarsa 算法agent.learn(obs, action, reward, next_obs, next_action, done)action = next_actionobs = next_obs  # 存储上一个观察值total_reward += rewardtotal_steps += 1 # 计算step数if render:env.render() #渲染新的一帧图形if done:breakreturn total_reward, total_stepsdef test_episode(env, agent):total_reward = 0obs = env.reset()while True:action = agent.predict(obs) # greedynext_obs, reward, done, _ = env.step(action)total_reward += rewardobs = next_obs# time.sleep(0.5)# env.render()if done:breakreturn total_reward# 使用gym创建悬崖环境
env = gym.make("CliffWalking-v0")  # 0 up, 1 right, 2 down, 3 left# 创建一个agent实例，输入超参数
agent = SarsaAgent(obs_n=env.observation_space.n,act_n=env.action_space.n,learning_rate=0.1,gamma=0.9,e_greed=0.1)# 训练500个episode，打印每个episode的分数
for episode in range(500):ep_reward, ep_steps = run_episode(env, agent, False)print('Episode %s: steps = %s , reward = %.1f' % (episode, ep_steps, ep_reward))# 全部训练结束，查看算法效果
test_reward = test_episode(env, agent)
print('test reward = %.1f' % (test_reward))
agent.save()

运行结果

• 只保留最后一部分
  
• Q值表的部分
• 读取方法见此link