151 lines
5.7 KiB
Python
151 lines
5.7 KiB
Python
import numpy as np
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import torch
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import torch.nn.functional as F
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from torch.distributions import Normal
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from networks import PolicyNetwork, ValueNetwork
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class PPOAgent:
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"""
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PPO-Clip (Proximal Policy Optimization, Clipped version)
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Reference: Schulman et al., "Proximal Policy Optimization Algorithms", 2017.
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与 TRPO 的核心区别：
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- 不再使用共轭梯度 + 线搜索求解约束优化
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- 用 clip(ratio, 1-ε, 1+ε) 限制策略更新幅度
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- 支持多 epoch 的小批量更新（每次从经验池中采样）
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"""
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def __init__(
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self,
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state_dim,
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action_dim,
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action_bound,
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hidden_dim=128,
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gamma=0.99,
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tau=0.97,
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lr=3e-4,
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clip_eps=0.2,
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k_epochs=10,
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minibatch_size=64,
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critic_epochs=10,
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entropy_coef=0.0,
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):
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self.gamma = gamma
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self.tau = tau
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self.clip_eps = clip_eps # PPO-Clip 的裁剪范围 ε
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self.k_epochs = k_epochs # 每次更新对同一批数据的训练轮数
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self.minibatch_size = minibatch_size
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self.entropy_coef = entropy_coef # 熵正则化系数（可选，鼓励探索）
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self.actor = PolicyNetwork(state_dim, action_dim, action_bound, hidden_dim)
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self.critic = ValueNetwork(state_dim, hidden_dim)
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self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=lr)
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self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=lr)
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def get_action(self, state):
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state_tensor = torch.FloatTensor(state).unsqueeze(0)
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with torch.no_grad():
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dist = self.actor.evaluate(state_tensor)
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action = dist.sample()
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return action.squeeze(0).numpy()
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def _compute_advantages(self, rewards, values, masks):
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"""
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GAE (Generalized Advantage Estimation)
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"""
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returns = []
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gae = 0
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for i in reversed(range(len(rewards))):
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delta = rewards[i] + self.gamma * values[i + 1] * masks[i] - values[i]
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gae = delta + self.gamma * self.tau * masks[i] * gae
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returns.insert(0, gae + values[i])
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return returns
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def update(self, memory):
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"""
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PPO-Clip 更新
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Algorithm 1 (Schulman et al. 2017):
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for iteration=1, 2, ... do
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for actor=1, 2, ..., N do
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Run policy π_θold in environment for T timesteps
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Compute advantage estimates Aˆ1, ..., AˆT
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end for
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Optimize surrogate L wrt θ, with K epochs and minibatch size M ≤ NT
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θold ← θ
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end for
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"""
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# ---------- 1. 准备数据 ----------
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states = torch.FloatTensor(np.array([m[0] for m in memory]))
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actions = torch.FloatTensor(np.array([m[1] for m in memory]))
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rewards = [m[2] for m in memory]
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next_states = torch.FloatTensor(np.array([m[3] for m in memory]))
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masks = [m[4] for m in memory]
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# ---------- 2. GAE 优势估计 ----------
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with torch.no_grad():
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values = self.critic(states).squeeze().numpy().tolist()
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next_value = self.critic(next_states[-1].unsqueeze(0)).squeeze().item()
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values.append(next_value)
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returns = self._compute_advantages(rewards, values, masks)
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returns = torch.FloatTensor(returns)
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values = torch.FloatTensor(values[:-1])
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advantages = returns - values
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advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
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# ---------- 3. 训练 Critic (Value Network) ----------
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for _ in range(self.critic_epochs):
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critic_loss = F.mse_loss(self.critic(states).squeeze(), returns)
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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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# ---------- 4. 训练 Actor (PPO-Clip 损失) ----------
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# 在 no_grad 下记录旧策略的对数概率（对应 Algorithm 1 中的 π_θold）
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with torch.no_grad():
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old_dist = self.actor.evaluate(states)
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old_log_probs = old_dist.log_prob(actions).sum(dim=1)
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dataset_size = states.size(0)
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indices = np.arange(dataset_size)
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for _ in range(self.k_epochs):
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# 每轮随机打乱，分成多个 minibatch
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np.random.shuffle(indices)
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for start in range(0, dataset_size, self.minibatch_size):
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end = start + self.minibatch_size
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mb_idx = indices[start:end]
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mb_states = states[mb_idx]
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mb_actions = actions[mb_idx]
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mb_advantages = advantages[mb_idx]
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mb_old_log_probs = old_log_probs[mb_idx]
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# 当前策略的对数概率
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dist = self.actor.evaluate(mb_states)
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log_probs = dist.log_prob(mb_actions).sum(dim=1)
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# 概率比 r_t(θ) = π_θ(a|s) / π_θold(a|s)
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ratio = torch.exp(log_probs - mb_old_log_probs)
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# ---------- PPO-Clip 核心 ----------
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# L^CLIP(θ) = E[min(r_t(θ) * A_t, clip(r_t(θ), 1-ε, 1+ε) * A_t)]
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surr1 = ratio * mb_advantages
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surr2 = torch.clamp(ratio, 1 - self.clip_eps, 1 + self.clip_eps) * mb_advantages
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policy_loss = -torch.min(surr1, surr2).mean()
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# ---------------------------------
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# 可选：熵正则化（鼓励探索）
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entropy_loss = -self.entropy_coef * dist.entropy().mean() if self.entropy_coef > 0 else 0
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total_loss = policy_loss + entropy_loss
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self.actor_optimizer.zero_grad()
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total_loss.backward()
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self.actor_optimizer.step()
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