如何解决深度 Q 网络中的大规模行动空间问题？

要解决深度Q网络中的大规模行动空间问题，可以使用以下方法：

1. 使用DQN的经验回放：通过保存智能体的经验，并从经验池中随机采样进行训练，可以减少训练样本的相关性，从而提高训练效率。以下是使用经验回放的示例代码：

# 定义经验回放的经验存储器
class ReplayMemory():
    def __init__(self, capacity):
        self.capacity = capacity
        self.memory = []  # 存储经验的列表

    # 添加新的经验
    def push(self, state, action, next_state, reward):
        experience = (state, action, next_state, reward)
        self.memory.append(experience)
        if len(self.memory) > self.capacity:
            del self.memory[0]  # 如果存储器已满，删除最早的经验

    # 随机采样一批经验
    def sample(self, batch_size):
        experiences = random.sample(self.memory, batch_size)
        states, actions, next_states, rewards = zip(*experiences)
        return states, actions, next_states, rewards

# 在训练过程中使用经验回放
memory = ReplayMemory(capacity=10000)  # 创建经验回放的经验存储器

# 在每次与环境互动后，将经验添加到经验存储器中
state = env.reset()
while True:
    action = agent.select_action(state)
    next_state, reward, done, _ = env.step(action)
    memory.push(state, action, next_state, reward)
    state = next_state
    if done:
        break

# 随机采样一批经验进行训练
batch_size = 64
states, actions, next_states, rewards = memory.sample(batch_size)
# 使用这些经验进行网络的训练

2. 使用优先级经验回放：根据经验的优先级进行采样，优先选择那些对网络权重的改变产生更大影响的经验进行训练。以下是使用优先级经验回放的示例代码：

# 定义优先级经验回放的经验存储器
class PriorityReplayMemory():
    def __init__(self, capacity):
        self.capacity = capacity
        self.memory = []  # 存储经验的列表
        self.priorities = []  # 存储经验的优先级

    # 添加新的经验
    def push(self, state, action, next_state, reward, priority):
        experience = (state, action, next_state, reward)
        self.memory.append(experience)
        self.priorities.append(priority)
        if len(self.memory) > self.capacity:
            del self.memory[0]  # 如果存储器已满，删除最早的经验
            del self.priorities[0]

    # 根据优先级随机采样一批经验
    def sample(self, batch_size):
        priorities = np.array(self.priorities)
        probs = priorities / priorities.sum()  # 计算采样概率
        indices = np.random.choice(len(self.memory), batch_size, p=probs)
        experiences = [self.memory[i] for i in indices]
        states, actions, next_states, rewards = zip(*experiences)
        return states, actions, next_states, rewards

# 在训练过程中使用优先级经验回放
memory = PriorityReplayMemory(capacity=10000)  # 创建优先级经验回放的经验存储器

# 在每次与环境互动后，将经验添加到经验存储器中，并计算优先级
state = env.reset()
while True:
    action = agent.select_action(state)
    next_state, reward, done, _ = env.step(action)
    priority = calculate_priority(state, action, next_state, reward)  # 根据经验计算优先级
    memory.push(state, action, next_state, reward, priority)
    state = next_state
    if done:
        break

# 根据优先级