强化学习在环境保护领域的应用

    import numpy as np
    
    # 初始化参数
    n_states = 10
    n_actions = 3
    alpha = 0.1
    gamma = 0.99
    epsilon = 0.1
    n_episodes = 1000
    
    # 初始化Q表
    Q = np.zeros((n_states, n_actions))
    
    # 状态转移函数
    def transition(s, a):
    # 根据当前状态和行动，返回新状态和奖励
    pass
    
    # Q学习算法
    for episode in range(n_episodes):
    s = 0  # 初始状态
    while True:
        # 选择行动
        if np.random.rand() < epsilon:
            a = np.random.randint(n_actions)
        else:
            a = np.argmax(Q[s])
    
        # 与环境交互
        s_next, r = transition(s, a)
    
        # 更新Q表
        Q[s, a] += alpha * (r + gamma * np.max(Q[s_next]) - Q[s, a])
    
        # 转移到下一个状态
        s = s_next
    
        # 判断是否结束
        if s == n_states - 1:
            break
    
    # 输出最优策略
    print("Optimal policy:", np.argmax(Q, axis=1))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import numpy as np
    import torch
    import torch.nn as nn
    import torch.optim as optim
    from collections import deque
    import random
    
    # 定义神经网络
    class QNetwork(nn.Module):
    def __init__(self, state_size, action_size):
        super(QNetwork, self).__init__()
        self.fc1 = nn.Linear(state_size, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, action_size)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x
    
    # 初始化参数
    state_size = 10
    action_size = 3
    alpha = 0.1
    gamma = 0.99
    epsilon = 0.1
    n_episodes = 1000
    buffer_size = 10000
    batch_size = 64
    update_freq = 100
    
    # 初始化神经网络和优化器
    q_net = QNetwork(state_size, action_size)
    target_net = QNetwork(state_size, action_size)
    optimizer = optim.Adam(q_net.parameters(), lr=alpha)
    
    # 初始化经验回放缓冲区
    buffer = deque(maxlen=buffer_size)
    
    # 状态转移函数
    def transition(s, a):
    # 根据当前状态和行动，返回新状态和奖励
    pass
    
    # DQN算法
    for episode in range(n_episodes):
    s = 0  # 初始状态
    while True:
        # 选择行动
        if np.random.rand() < epsilon:
            a = np.random.randint(action_size)
        else:
            a = np.argmax(q_net(torch.tensor(s, dtype=torch.float32)).detach().numpy())
    
        # 与环境交互
        s_next, r = transition(s, a)
    
        # 存储经验
        buffer.append((s, a, r, s_next))
    
        # 从缓冲区中随机抽取经验
        if len(buffer) >= batch_size:
            batch = random.sample(buffer, batch_size)
            states, actions, rewards, next_states = zip(*batch)
    
            # 计算目标Q值
            target_q = target_net(torch.tensor(next_states, dtype=torch.float32)).detach().max(1)[0]
            target_q = torch.tensor(rewards, dtype=torch.float32) + gamma * target_q
    
            # 计算当前Q值
            current_q = q_net(torch.tensor(states, dtype=torch.float32)).gather(1, torch.tensor(actions).unsqueeze(1))
    
            # 更新神经网络
            loss = nn.MSELoss()(current_q, target_q.unsqueeze(1))
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
    
        # 更新目标网络
        if episode % update_freq == 0:
            target_net.load_state_dict(q_net.state_dict())
    
        # 转移到下一个状态
        s = s_next
    
        # 判断是否结束
        if s == state_size - 1:
            break
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读