深度 Qlearning：在智能医疗诊断中的应用

 * 根据当前状态,选择一个行为(exploitation或exploration)
 * 执行选择的行为,获得下一个状态和即时奖励
 * 更新Q值函数:  

    import torch
    import torch.nn as nn
    import torch.optim as optim
    import numpy as np
    
    # 定义Q网络
    class QNetwork(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(QNetwork, self).__init__()
        self.fc1 = nn.Linear(state_dim, 64)
        self.fc2 = nn.Linear(64, action_dim)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x
    
    # 定义DQN算法
    class DQN:
    def __init__(self, state_dim, action_dim, gamma=0.99, epsilon=1.0, epsilon_min=0.01, epsilon_decay=0.995, lr=0.001, batch_size=64, buffer_size=10000):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.gamma = gamma
        self.epsilon = epsilon
        self.epsilon_min = epsilon_min
        self.epsilon_decay = epsilon_decay
        self.lr = lr
        self.batch_size = batch_size
        self.buffer_size = buffer_size
    
        self.q_network = QNetwork(state_dim, action_dim)
        self.target_network = QNetwork(state_dim, action_dim)
        self.target_network.load_state_dict(self.q_network.state_dict())
        self.optimizer = optim.Adam(self.q_network.parameters(), lr=self.lr)
        self.replay_buffer = []
    
    def get_action(self, state):
        if np.random.rand() < self.epsilon:
            return np.random.randint(self.action_dim)
        else:
            state = torch.tensor(state, dtype=torch.float32).unsqueeze(0)
            q_values = self.q_network(state)
            return torch.argmax(q_values).item()
    
    def update_replay_buffer(self, transition):
        self.replay_buffer.append(transition)
        if len(self.replay_buffer) > self.buffer_size:
            self.replay_buffer.pop(0)
    
    def update_q_network(self):
        if len(self.replay_buffer) < self.batch_size:
            return
    
        transitions = np.random.choice(self.replay_buffer, size=self.batch_size)
        state_batch, action_batch, reward_batch, next_state_batch, done_batch = zip(*transitions)
    
        state_batch = torch.tensor(state_batch, dtype=torch.float32)
        action_batch = torch.tensor(action_batch, dtype=torch.int64).unsqueeze(1)
        reward_batch = torch.tensor(reward_batch, dtype=torch.float32)
        next_state_batch = torch.tensor(next_state_batch, dtype=torch.float32)
        done_batch = torch.tensor(np.invert(done_batch).astype(np.float32))
    
        q_values = self.q_network(state_batch).gather(1, action_batch)
        next_q_values = self.target_network(next_state_batch).max(1)[0].detach()
        expected_q_values = reward_batch + self.gamma * next_q_values * done_batch
    
        loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1))
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
    
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay
    
        if len(self.replay_buffer) % 1000 == 0:
            self.target_network.load_state_dict(self.q_network.state_dict())
    
    # 训练DQN算法
    env = ... # 初始化环境
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n
    agent = DQN(state_dim, action_dim)
    
    for episode in range(num_episodes):
    state = env.reset()
    done = False
    while not done:
        action = agent.get_action(state)
        next_state, reward, done, _ = env.step(action)
        agent.update_replay_buffer((state, action, reward, next_state, done))
        agent.update_q_network()
        state = next_state
    
    # 打印episode reward等信息