RL_TRPO/agent/ppo.py

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
from networks import PolicyNetwork, ValueNetwork


class PPOAgent:
    """
    PPO-Clip (Proximal Policy Optimization, Clipped version)

    Reference: Schulman et al., "Proximal Policy Optimization Algorithms", 2017.

    与 TRPO 的核心区别：
    - 不再使用共轭梯度 + 线搜索求解约束优化
    - 用 clip(ratio, 1-ε, 1+ε) 限制策略更新幅度
    - 支持多 epoch 的小批量更新（每次从经验池中采样）
    """

    def __init__(
        self,
        state_dim,
        action_dim,
        action_bound,
        hidden_dim=128,
        gamma=0.99,
        tau=0.95,
        actor_lr=3e-4,
        critic_lr=1e-3,
        clip_eps=0.2,
        k_epochs=10,
        minibatch_size=64,
        max_grad_norm=0.5,
    ):
        self.gamma = gamma
        self.tau = tau
        self.clip_eps = clip_eps        # PPO-Clip 的裁剪范围 ε
        self.k_epochs = k_epochs         # 每次更新对同一批数据的训练轮数
        self.minibatch_size = minibatch_size
        self.max_grad_norm = max_grad_norm  # 梯度裁剪阈值

        self.actor = PolicyNetwork(state_dim, action_dim, action_bound, hidden_dim)
        self.critic = ValueNetwork(state_dim, hidden_dim)

        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=actor_lr)
        self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=critic_lr)

    def get_action(self, state):
        state_tensor = torch.FloatTensor(state).unsqueeze(0)
        with torch.no_grad():
            dist = self.actor.evaluate(state_tensor)
            action = dist.sample()
        return action.squeeze(0).numpy()

    def get_value(self, state):
        with torch.no_grad():
            return self.critic(torch.FloatTensor(state).unsqueeze(0)).squeeze().item()

    def _compute_advantages(self, rewards, values, masks):
        """
        GAE (Generalized Advantage Estimation)
        Eq.(11): Â_t = δ_t + (γλ)δ_{t+1} + ... + (γλ)^{T-t+1} δ_{T-1}
        Eq.(12): δ_t = r_t + γV(s_{t+1}) - V(s_t)
        """
        returns = []
        gae = 0
        for i in reversed(range(len(rewards))):
            delta = rewards[i] + self.gamma * values[i + 1] * masks[i] - values[i]
            gae = delta + self.gamma * self.tau * masks[i] * gae
            returns.insert(0, gae + values[i])
        return returns

    def update(self, memory):
        """
        PPO-Clip 更新 — 对齐论文 Algorithm 1 和 Eq.(9)

        关键改动（相比旧版）:
        1. Actor 和 Critic 在同一个 minibatch 循环内联合训练
           L = L^CLIP - c1 * L^VF   (Eq.9，c2=0 不加熵)
        2. 梯度裁剪 (max_grad_norm)
        """
        # ---------- 1. 准备数据 ----------
        states = torch.FloatTensor(np.array([m[0] for m in memory]))
        actions = torch.FloatTensor(np.array([m[1] for m in memory]))
        rewards = [m[2] for m in memory]
        next_states = torch.FloatTensor(np.array([m[3] for m in memory]))
        masks = [m[4] for m in memory]

        # ---------- 2. GAE 优势估计 ----------
        with torch.no_grad():
            values = self.critic(states).squeeze().numpy().tolist()
            next_value = self.critic(next_states[-1].unsqueeze(0)).squeeze().item()
            values.append(next_value)

        returns = self._compute_advantages(rewards, values, masks)
        returns = torch.FloatTensor(returns)
        values = torch.FloatTensor(values[:-1])
        advantages = returns - values
        advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)

        # ---------- 3. 记录旧策略 π_θold ----------
        with torch.no_grad():
            old_dist = self.actor.evaluate(states)
            old_log_probs = old_dist.log_prob(actions).sum(dim=1)

        # ---------- 4. K epochs × minibatch 联合训练 ----------
        dataset_size = states.size(0)
        indices = np.arange(dataset_size)

        for _ in range(self.k_epochs):
            np.random.shuffle(indices)

            for start in range(0, dataset_size, self.minibatch_size):
                end = start + self.minibatch_size
                mb_idx = indices[start:end]

                mb_states = states[mb_idx]
                mb_actions = actions[mb_idx]
                mb_advantages = advantages[mb_idx]
                mb_returns = returns[mb_idx]
                mb_old_log_probs = old_log_probs[mb_idx]

                # --- Actor: PPO-Clip 损失 ---
                dist = self.actor.evaluate(mb_states)
                log_probs = dist.log_prob(mb_actions).sum(dim=1)
                ratio = torch.exp(log_probs - mb_old_log_probs)

                surr1 = ratio * mb_advantages
                surr2 = torch.clamp(ratio, 1 - self.clip_eps, 1 + self.clip_eps) * mb_advantages
                policy_loss = -torch.min(surr1, surr2).mean()

                # --- Critic: 价值函数 MSE 损失 ---
                value_pred = self.critic(mb_states).squeeze()
                critic_loss = F.mse_loss(value_pred, mb_returns)

                # Actor 更新（带梯度裁剪，防止策略大幅跳变）
                self.actor_optimizer.zero_grad()
                policy_loss.backward()
                nn.utils.clip_grad_norm_(self.actor.parameters(), self.max_grad_norm)
                self.actor_optimizer.step()

                # Critic 更新（不裁剪梯度，Adam 自适应处理大梯度即可）
                self.critic_optimizer.zero_grad()
                critic_loss.backward()
                self.critic_optimizer.step()