192 lines
7.4 KiB
Python
192 lines
7.4 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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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=0.95,
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actor_lr=3e-4,
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critic_lr=1e-3,
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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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max_grad_norm=0.5,
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device="cpu",
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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.max_grad_norm = max_grad_norm # 梯度裁剪阈值
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self.device = torch.device(device)
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self.actor = PolicyNetwork(state_dim, action_dim, action_bound, hidden_dim).to(self.device)
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self.critic = ValueNetwork(state_dim, hidden_dim).to(self.device)
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self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=actor_lr)
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self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=critic_lr)
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def _to_tensor(self, array_like):
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return torch.as_tensor(array_like, dtype=torch.float32, device=self.device)
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def get_action(self, state):
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state_tensor = self._to_tensor(state)
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single_state = state_tensor.ndim == 1
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if single_state:
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state_tensor = state_tensor.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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action_np = action.detach().cpu().numpy()
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return action_np[0] if single_state else action_np
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def get_value(self, state):
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state_tensor = self._to_tensor(state)
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single_state = state_tensor.ndim == 1
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if single_state:
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state_tensor = state_tensor.unsqueeze(0)
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with torch.no_grad():
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values = self.critic(state_tensor).squeeze(-1)
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values_np = values.detach().cpu().numpy()
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return float(values_np[0]) if single_state else values_np
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def _compute_advantages(self, rewards, values, next_values, masks):
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"""
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GAE (Generalized Advantage Estimation)
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Eq.(11): Â_t = δ_t + (γλ)δ_{t+1} + ... + (γλ)^{T-t+1} δ_{T-1}
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Eq.(12): δ_t = r_t + γV(s_{t+1}) - V(s_t)
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"""
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advantages = torch.zeros_like(rewards, device=self.device)
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gae = torch.zeros(rewards.size(1), dtype=torch.float32, device=self.device)
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for i in reversed(range(rewards.size(0))):
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delta = rewards[i] + self.gamma * next_values[i] * masks[i] - values[i]
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gae = delta + self.gamma * self.tau * masks[i] * gae
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advantages[i] = gae
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returns = advantages + values
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return returns, advantages
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def update(self, memory):
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"""
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PPO-Clip 更新 — 对齐论文 Algorithm 1 和 Eq.(9)
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关键改动（相比旧版）:
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1. Actor 和 Critic 在同一个 minibatch 循环内联合训练
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L = L^CLIP - c1 * L^VF (Eq.9，c2=0 不加熵)
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2. 梯度裁剪 (max_grad_norm)
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"""
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if not memory:
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return {}
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# ---------- 1. 准备数据 ----------
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states = self._to_tensor(np.asarray(memory["states"]))
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actions = self._to_tensor(np.asarray(memory["actions"]))
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rewards = self._to_tensor(np.asarray(memory["rewards"]))
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next_states = self._to_tensor(np.asarray(memory["next_states"]))
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masks = self._to_tensor(np.asarray(memory["masks"]))
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# ---------- 2. GAE 优势估计 ----------
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with torch.no_grad():
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rollout_steps, num_envs = rewards.shape
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flat_states = states.reshape(rollout_steps * num_envs, -1)
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flat_next_states = next_states.reshape(rollout_steps * num_envs, -1)
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values = self.critic(flat_states).squeeze(-1).reshape(rollout_steps, num_envs)
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next_values = self.critic(flat_next_states).squeeze(-1).reshape(rollout_steps, num_envs)
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returns, advantages = self._compute_advantages(rewards, values, next_values, masks)
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advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
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states = flat_states
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actions = actions.reshape(rollout_steps * num_envs, -1)
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returns = returns.reshape(rollout_steps * num_envs)
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advantages = advantages.reshape(rollout_steps * num_envs)
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# ---------- 3. 记录旧策略 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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# ---------- 4. K epochs × minibatch 联合训练 ----------
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dataset_size = states.size(0)
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indices = np.arange(dataset_size)
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policy_loss_value = 0.0
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critic_loss_value = 0.0
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entropy_value = 0.0
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minibatch_updates = 0
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for _ in range(self.k_epochs):
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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 = torch.as_tensor(indices[start:end], device=self.device, dtype=torch.long)
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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_returns = returns[mb_idx]
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mb_old_log_probs = old_log_probs[mb_idx]
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# --- Actor: PPO-Clip 损失 ---
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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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ratio = torch.exp(log_probs - mb_old_log_probs)
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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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# --- Critic: 价值函数 MSE 损失 ---
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value_pred = self.critic(mb_states).squeeze()
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critic_loss = F.mse_loss(value_pred, mb_returns)
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# Actor 更新（带梯度裁剪，防止策略大幅跳变）
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self.actor_optimizer.zero_grad()
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policy_loss.backward()
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nn.utils.clip_grad_norm_(self.actor.parameters(), self.max_grad_norm)
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self.actor_optimizer.step()
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# Critic 更新（不裁剪梯度，Adam 自适应处理大梯度即可）
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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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policy_loss_value += float(policy_loss.detach().item())
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critic_loss_value += float(critic_loss.detach().item())
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entropy_value += float(dist.entropy().sum(dim=1).mean().detach().item())
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minibatch_updates += 1
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divisor = max(1, minibatch_updates)
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return {
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"policy_loss": policy_loss_value / divisor,
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"critic_loss": critic_loss_value / divisor,
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"entropy": entropy_value / divisor,
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}
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