double dqn伪代码

### Double DQN 算法伪代码 Deep Q-Network (DQN) 是一种结合深度学习和强化学习的方法，用于解决高维状态空间中的决策问题。Double DQN 对原始 DQN 进行改进，通过分离目标网络和在线网络来减少过估计问题[^1]。 以下是 Double DQN 的伪代码实现： #### 初始化阶段 ```python # 初始化参数 Initialize replay memory M to capacity N Initialize online network parameters θ with random weights Initialize target network parameters θ' ← θ Set learning rate α, discount factor γ, and exploration probability ε ``` #### 训练过程 ```python for episode in range(total_episodes): Initialize state s while not done: With probability ε select a random action a otherwise select a = argmax_a Q(s,a;θ) Execute action a, observe reward r and next state s' Store transition (s, a, r, s') in replay memory M Sample random minibatch of transitions from M: {(si, ai, ri, si')} Set yi = { r_i if terminal, r_i + γ * max_a' Q(si',a';θ') otherwise } Perform gradient descent step on loss function L(θ) = E[(yi - Q(si,ai;θ))^2] Update target network periodically or softly by setting θ' ← τ*θ + (1-τ)*θ' s ← s' ``` 上述伪代码描述了 Double DQN 的核心机制： 1. 使用两个神经网络分别作为 **online network** 和 **target network**。 2. Target network 参数定期更新为 online network 参数的副本。 3. 在计算目标值 \( y \) 时，利用 target network 来评估动作价值函数的最大值，从而降低过估计偏差。 #### Python 实现片段 以下是一个简单的 Double DQN 更新逻辑示例： ```python import torch import torch.nn as nn import torch.optim as optim class DDQNAgent: def __init__(self, state_size, action_size): self.online_net = nn.Sequential( nn.Linear(state_size, 64), nn.ReLU(), nn.Linear(64, action_size) ) self.target_net = nn.Sequential( nn.Linear(state_size, 64), nn.ReLU(), nn.Linear(64, action_size) ) self.optimizer = optim.Adam(self.online_net.parameters(), lr=0.001) def update_target_network(self): self.target_net.load_state_dict(self.online_net.state_dict()) def compute_loss(self, batch): states, actions, rewards, next_states, dones = batch current_q_values = self.online_net(states).gather(1, actions.unsqueeze(-1)).squeeze() with torch.no_grad(): best_actions = self.online_net(next_states).argmax(dim=1) next_q_values = self.target_net(next_states).gather(1, best_actions.unsqueeze(-1)).squeeze() targets = rewards + gamma * next_q_values * (1 - dones) loss = nn.MSELoss()(current_q_values, targets.detach()) return loss def train_step(self, batch): loss = self.compute_loss(batch) self.optimizer.zero_grad() loss.backward() self.optimizer.step() agent = DDQNAgent(state_size=..., action_size=...) ``` ---