请编写一个Dyna-Q算法的代码

好的，以下是一个简单的Dyna-Q算法的Python代码实现：

import numpy as np

# 定义环境
n_states = 6
n_actions = 2
T = np.zeros((n_states, n_actions, n_states))
R = np.zeros((n_states, n_actions, n_states))
T[0, 0, :] = [0.7, 0.3, 0, 0, 0, 0]
T[0, 1, :] = [0, 0.3, 0.7, 0, 0, 0]
T[1, :, :] = [0.3, 0.7, 0, 0, 0, 0]
T[2, :, :] = [0, 0, 0, 0.8, 0, 0.2]
T[3, :, :] = [0, 0, 0, 0.4, 0.6, 0]
T[4, :, :] = [0, 0, 0, 0, 0.5, 0.5]
T[5, :, :] = [0, 0, 0, 0, 0, 1]
R[0, 0, :] = [-0.1, -0.1, 0, 0, 0, 1]
R[0, 1, :] = [0, -0.1, -0.1, 0, 0, 1]
R[1, :, :] = [-0.1, 0, 0, 0, 0, 1]
R[2, :, :] = [0, 0, 0, -0.1, 0, 1]
R[3, :, :] = [0, 0, 0, -0.1, -0.1, 1]
R[4, :, :] = [0, 0, 0, 0, -0.1, 1]
R[5, :, :] = [0, 0, 0, 0, 0, 0]

# 定义参数
alpha = 0.5
gamma = 0.9
epsilon = 0.1
n_planning = 5
n_episodes = 1000

# 初始化Q值和模型
Q = np.zeros((n_states, n_actions))
model_T = np.zeros((n_states, n_actions, n_states))
model_R = np.zeros((n_states, n_actions, n_states))

# Dyna-Q算法主循环
for i in range(n_episodes):
    # 初始化状态
    state = np.random.randint(n_states)

    # 每个episode内进行多次更新
    for j in range(100):
        # 按照epsilon-greedy策略选择动作
        if np.random.uniform() < epsilon:
            action = np.random.randint(n_actions)
        else:
            action = np.argmax(Q[state, :])

        # 执行动作并观察奖励和新状态
        new_state = np.random.choice(n_states, p=T[state, action, :])
        reward = R[state, action, new_state]

        # 更新Q值
        Q[state, action] = Q[state, action] + alpha * (reward + gamma * np.max(Q[new_state, :]) - Q[state, action])

        # 更新模型
        model_T[state, action, new_state] = model_T[state, action, new_state] + 1
        model_R[state, action, new_state] = reward

        # planning
        for k in range(n_planning):
            # 从模型中采样一个先前经历过的状态和动作
            prev_state = np.random.randint(n_states)
            prev_action = np.random.randint(n_actions)

            # 通过模型预测下一个状态和奖励
            next_state = np.argmax(model_T[prev_state, prev_action, :])
            reward = model_R[prev_state, prev_action, next_state]

            # 更新Q值
            Q[prev_state, prev_action] = Q[prev_state, prev_action] + alpha * (reward + gamma * np.max(Q[next_state, :]) - Q[prev_state, prev_action])

        # 更新状态
        state = new_state

以上代码实现了一个简单的Dyna-Q算法，用于解决一个简单的MDP问题。算法的主要思路是利用模型（T和R）来生成先前经历的状态和动作的概率分布，并对这些状态和动作进行多次更新，以提高算法的性能。在代码实现中，我们使用了一个`model_T`和`model_R`来存储从环境中观测到的先前经历的状态和动作，每次planning时从中采样。