q-learning实现cliffwalking-v0代码python

以下是使用Q-learning算法实现Cliff Walking游戏的Python代码：

import numpy as np
import gym

env = gym.make("CliffWalking-v0")

num_states = env.observation_space.n
num_actions = env.action_space.n
Q = np.zeros((num_states, num_actions))

num_episodes = 5000
epsilon = 0.1
alpha = 0.5
gamma = 0.99

for i in range(num_episodes):
    observation = env.reset()
    done = False
    while not done:
        if np.random.uniform() < epsilon:
            action = env.action_space.sample()
        else:
            action = np.argmax(Q[observation, :])
        next_observation, reward, done, info = env.step(action)
        Q[observation, action] = Q[observation, action] + alpha * (reward + gamma * np.max(Q[next_observation, :]) - Q[observation, action])
        observation = next_observation

print("Q-table:")
print(Q)

def play(Q):
    observation = env.reset()
    done = False
    while not done:
        env.render()
        action = np.argmax(Q[observation, :])
        observation, reward, done, info = env.step(action)
    env.render()
    print("Reward:", reward)

play(Q)

请注意，这段代码使用OpenAI Gym库中的Cliff Walking环境，并使用Q-learning算法实现Agent的决策过程。Agent的决策基于Q值函数，使用epsilon贪心策略探索和exploitation。代码训练Q表格并输出结果，最后演示游戏并输出最终的奖励。