重构 PPO/TRPO 训练流程并添加对比绘图
- PPO: 改为 Actor/Critic 联合小批量训练，新增梯度裁剪 (max_grad_norm)， 分离 actor_lr/critic_lr，添加 get_value()，GAE 部分补充论文公式注释 - TRPO: 添加 get_value()，调整 tau 从 0.97 到 0.95 - Networks: 移除 PolicyNet 输出层的 tanh，初始化 log_std=0 以增强探索 - Main: 抽取 train_agent() 通用训练函数，新增 TRPO 训练和 PPO vs TRPO 对比曲线图（原始曲线 + 滑动平均平滑曲线）
This commit is contained in:
+34
-38
@@ -1,5 +1,6 @@
|
||||
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
|
||||
@@ -24,26 +25,26 @@ class PPOAgent:
|
||||
action_bound,
|
||||
hidden_dim=128,
|
||||
gamma=0.99,
|
||||
tau=0.97,
|
||||
lr=3e-4,
|
||||
tau=0.95,
|
||||
actor_lr=3e-4,
|
||||
critic_lr=1e-3,
|
||||
clip_eps=0.2,
|
||||
k_epochs=10,
|
||||
minibatch_size=64,
|
||||
critic_epochs=10,
|
||||
entropy_coef=0.0,
|
||||
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.entropy_coef = entropy_coef # 熵正则化系数（可选，鼓励探索）
|
||||
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=lr)
|
||||
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=lr)
|
||||
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)
|
||||
@@ -52,9 +53,15 @@ class PPOAgent:
|
||||
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
|
||||
@@ -66,17 +73,12 @@ class PPOAgent:
|
||||
|
||||
def update(self, memory):
|
||||
"""
|
||||
PPO-Clip 更新
|
||||
PPO-Clip 更新 — 对齐论文 Algorithm 1 和 Eq.(9)
|
||||
|
||||
Algorithm 1 (Schulman et al. 2017):
|
||||
for iteration=1, 2, ... do
|
||||
for actor=1, 2, ..., N do
|
||||
Run policy π_θold in environment for T timesteps
|
||||
Compute advantage estimates Aˆ1, ..., AˆT
|
||||
end for
|
||||
Optimize surrogate L wrt θ, with K epochs and minibatch size M ≤ NT
|
||||
θold ← θ
|
||||
end for
|
||||
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]))
|
||||
@@ -97,24 +99,16 @@ class PPOAgent:
|
||||
advantages = returns - values
|
||||
advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
|
||||
|
||||
# ---------- 3. 训练 Critic (Value Network) ----------
|
||||
for _ in range(self.critic_epochs):
|
||||
critic_loss = F.mse_loss(self.critic(states).squeeze(), returns)
|
||||
self.critic_optimizer.zero_grad()
|
||||
critic_loss.backward()
|
||||
self.critic_optimizer.step()
|
||||
|
||||
# ---------- 4. 训练 Actor (PPO-Clip 损失) ----------
|
||||
# 在 no_grad 下记录旧策略的对数概率（对应 Algorithm 1 中的 π_θold）
|
||||
# ---------- 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):
|
||||
# 每轮随机打乱，分成多个 minibatch
|
||||
np.random.shuffle(indices)
|
||||
|
||||
for start in range(0, dataset_size, self.minibatch_size):
|
||||
@@ -124,27 +118,29 @@ class PPOAgent:
|
||||
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]
|
||||
|
||||
# 当前策略的对数概率
|
||||
# --- Actor: PPO-Clip 损失 ---
|
||||
dist = self.actor.evaluate(mb_states)
|
||||
log_probs = dist.log_prob(mb_actions).sum(dim=1)
|
||||
|
||||
# 概率比 r_t(θ) = π_θ(a|s) / π_θold(a|s)
|
||||
ratio = torch.exp(log_probs - mb_old_log_probs)
|
||||
|
||||
# ---------- PPO-Clip 核心 ----------
|
||||
# L^CLIP(θ) = E[min(r_t(θ) * A_t, clip(r_t(θ), 1-ε, 1+ε) * A_t)]
|
||||
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()
|
||||
# ---------------------------------
|
||||
|
||||
# 可选：熵正则化（鼓励探索）
|
||||
entropy_loss = -self.entropy_coef * dist.entropy().mean() if self.entropy_coef > 0 else 0
|
||||
|
||||
total_loss = policy_loss + entropy_loss
|
||||
# --- 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()
|
||||
total_loss.backward()
|
||||
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()
|
||||
|
||||
Reference in New Issue
Block a user