Hongru
6b8156eceb
- 新增 RL_Algothrithms 模块，包含 A2C、QAC 智能体 - 添加 SAC 章节笔记和 C10 笔记 - 上传训练结果图片 - 完善 README 与 .gitignore
60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
import torch
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import torch.optim as optim
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import torch.nn.functional as F
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import torch.distributions as distributions
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from networks import Actor, QCritic
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class QACAgent:
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def __init__(self, state_dim, action_dim, device, actor_lr=0.001, critic_lr=0.002, gamma=0.99):
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# 接收设备参数，确保网络挂载在 GPU 或 CPU 上
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self.device = device
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self.gamma = gamma
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# 实例化网络并移动到指定设备
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self.actor = Actor(state_dim, action_dim).to(self.device)
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self.critic = QCritic(state_dim, action_dim).to(self.device)
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self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=actor_lr)
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self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=critic_lr)
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def select_action(self, state):
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# 推理时禁用梯度图计算，加快速度
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with torch.no_grad():
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state_tensor = torch.FloatTensor(state).unsqueeze(0).to(self.device)
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action_probs = self.actor(state_tensor)
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action = distributions.Categorical(action_probs).sample().item()
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return action
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def update(self, state, action, reward, next_state, next_action, done):
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# 将数据转换为张量并送入 GPU
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state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
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next_state = torch.FloatTensor(next_state).unsqueeze(0).to(self.device)
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reward = torch.FloatTensor([reward]).unsqueeze(0).to(self.device)
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# --- Critic 更新 ---
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q_values = self.critic(state)
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current_q = q_values[0, action]
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next_q_values = self.critic(next_state).detach()
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next_q = next_q_values[0, next_action]
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# 计算 TD 目标
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td_target = reward + self.gamma * next_q * (1 - int(done))
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critic_loss = F.mse_loss(current_q, td_target.squeeze())
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self.critic_optimizer.zero_grad()
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critic_loss.backward()
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self.critic_optimizer.step()
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# --- Actor 更新 ---
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action_probs = self.actor(state)
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dist = distributions.Categorical(action_probs)
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log_prob = dist.log_prob(torch.tensor([action]).to(self.device))
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# Actor 梯度上升目标：ln(pi) * Q(s, a)
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actor_loss = -(log_prob * current_q.detach())
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self.actor_optimizer.zero_grad()
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actor_loss.backward()
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self.actor_optimizer.step()
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