# 假设有一个策略网络 PolicyNet，一个值函数网络 ValueNet，以及优化器 optimizer
def train(policy_net, value_net, optimizer, states, actions, rewards, next_states, epochs=10):
# 初始化优势估计（这里省略，通常需要前向传播值函数网络并计算）
advantages = compute_advantages(rewards, next_states, value_net)
for epoch in range(epochs):
for state, action, advantage in zip(states, actions, advantages):
# 预测旧的动作概率和值
old_action_probs, old_value = policy_net(state), value_net(state)
old_action_log_prob = log_probability(old_action_probs, action)
# 计算新的动作概率和值
new_action_probs, new_value = policy_net(state), value_net(state)
new_action_log_prob = log_probability(new_action_probs, action)
# 计算比率
ratio = exp(new_action_log_prob - old_action_log_prob)
# PPO剪切机制
surr1 = ratio * advantage
surr2 = clip(ratio, 1 - epsilon, 1 + epsilon) * advantage
policy_loss = -torch.min(surr1, surr2).mean()
# 值函数损失（可选）
value_loss = mse_loss(new_value, target_value) # target_value 需要根据奖励计算
# 总损失
loss = policy_loss + value_loss * value_coef
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 注意：这里的 compute_advantages, log_probability, mse_loss 等函数需要你自己实现
# 这里的 epsilon 是PPO剪切机制中的超参数，用于控制策略更新的步长
