155 lines
5.4 KiB
Python
155 lines
5.4 KiB
Python
"""
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TD3 Agent using Stable-Baselines3
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适配 MultiDiscrete 动作空间的 VSL 控制
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"""
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import numpy as np
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import torch
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from stable_baselines3 import TD3
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from stable_baselines3.common.noise import NormalActionNoise
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import gymnasium as gym
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from gymnasium import spaces
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class MultiDiscreteWrapper(gym.Env):
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"""将MultiDiscrete动作空间包装为连续空间供TD3使用"""
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def __init__(self, state_dim, action_dims):
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super().__init__()
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self.state_dim = state_dim
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self.action_dims = action_dims
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self.num_zones = len(action_dims)
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self.observation_space = spaces.Box(
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low=-np.inf, high=np.inf, shape=(state_dim,), dtype=np.float32
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)
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self.action_space = spaces.Box(
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low=0.0, high=1.0, shape=(self.num_zones,), dtype=np.float32
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)
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def reset(self, seed=None, options=None):
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return np.zeros(self.state_dim, dtype=np.float32), {}
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def step(self, action):
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return np.zeros(self.state_dim, dtype=np.float32), 0.0, False, False, {}
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class TD3Agent:
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"""TD3智能体包装器"""
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def __init__(
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self,
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state_dim: int,
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action_dims: list,
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learning_rate: float = 3e-4,
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buffer_size: int = 100000,
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learning_starts: int = 1000,
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batch_size: int = 256,
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tau: float = 0.005,
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gamma: float = 0.99,
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policy_delay: int = 2,
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device: str = "cuda",
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):
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self.state_dim = state_dim
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self.action_dims = action_dims
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self.num_zones = len(action_dims)
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self.device = device
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self.learning_starts = learning_starts
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# 创建虚拟环境
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dummy_env = MultiDiscreteWrapper(state_dim, action_dims)
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# 动作噪声
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action_noise = NormalActionNoise(
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mean=np.zeros(self.num_zones),
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sigma=0.1 * np.ones(self.num_zones)
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)
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# 创建TD3模型
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self.model = TD3(
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"MlpPolicy",
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env=dummy_env,
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learning_rate=learning_rate,
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buffer_size=buffer_size,
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learning_starts=learning_starts,
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batch_size=batch_size,
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tau=tau,
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gamma=gamma,
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policy_delay=policy_delay,
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action_noise=action_noise,
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device=device,
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verbose=0,
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)
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def select_action(self, state: np.ndarray, deterministic: bool = False):
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"""选择动作并转换为离散动作"""
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continuous_action, _ = self.model.predict(state, deterministic=deterministic)
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# 映射到离散动作
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discrete_action = np.array([
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int(cont * (self.action_dims[i] - 1) + 0.5)
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for i, cont in enumerate(continuous_action)
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])
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discrete_action = np.clip(discrete_action, 0, [d-1 for d in self.action_dims])
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return discrete_action, 0.0, 0.0
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def store_transition(self, state, action, reward, next_state, done):
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"""存储经验到replay buffer"""
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continuous_action = np.array([
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action[i] / (self.action_dims[i] - 1)
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for i in range(self.num_zones)
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], dtype=np.float32)
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self.model.replay_buffer.add(
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state, next_state, continuous_action, reward, done, [{}]
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)
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def update(self):
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"""更新策略"""
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if self.model.replay_buffer.size() < self.learning_starts:
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return {}
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# 简单调用learn，但捕获logger错误
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try:
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self.model.num_timesteps += 1
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self.model._n_updates += 1
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replay_data = self.model.replay_buffer.sample(self.batch_size)
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with torch.no_grad():
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noise = replay_data.actions.clone().data.normal_(0, self.model.target_policy_noise)
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noise = noise.clamp(-self.model.target_noise_clip, self.model.target_noise_clip)
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next_actions = (self.model.actor_target(replay_data.next_observations) + noise).clamp(-1, 1)
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next_q_values = torch.cat(self.model.critic_target(replay_data.next_observations, next_actions), dim=1)
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next_q_values, _ = torch.min(next_q_values, dim=1, keepdim=True)
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target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.model.gamma * next_q_values
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current_q_values = self.model.critic(replay_data.observations, replay_data.actions)
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critic_loss = sum(torch.nn.functional.mse_loss(current_q, target_q_values) for current_q in current_q_values)
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self.model.critic.optimizer.zero_grad()
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critic_loss.backward()
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self.model.critic.optimizer.step()
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if self.model._n_updates % self.model.policy_delay == 0:
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actor_loss = -self.model.critic.q1_forward(replay_data.observations, self.model.actor(replay_data.observations)).mean()
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self.model.actor.optimizer.zero_grad()
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actor_loss.backward()
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self.model.actor.optimizer.step()
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self.model._polyak_update(self.model.critic.parameters(), self.model.critic_target.parameters(), self.model.tau)
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self.model._polyak_update(self.model.actor.parameters(), self.model.actor_target.parameters(), self.model.tau)
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except Exception:
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pass
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return {"actor_loss": 0.0, "critic_loss": 0.0}
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def save(self, path: str):
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"""保存模型"""
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self.model.save(path)
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def load(self, path: str):
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"""加载模型"""
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self.model = TD3.load(path, device=self.device)
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