🤞 这段时间在师姐的推荐下，学习了网上关于强化学习的 PyTorch 实战视频，小生自己也是跟着视频复现了一遍 PPO 算法，于是准备写一篇 Blog 来记录学习心得
框架：PyTorch
游戏：LunarLander-v2
上一次运行成功时间：2025-01-20
（本文里的代码是小生重新手打的，未复制粘贴，可能存在拼写错误，望见谅）
1. 📦 导入必要的包
import torch
import torch.nn as nn
from torch.distributions.categorical import Categorical
import gym
import numpy as np
# 导完包之后顺手定义一下 device
device = torch.device(“cuda:0” if torch.cuda.is_available() else "cpu")
⚠️ 注意：下载 gym 的时候一定要记得安装一下 box2d 包，否则会报错。如下所示：
pip install box2d box2d-kengz
这里顺便列一下 requirements:
- pytorch: 2.5.0
- gym: 0.26.2
- box2d: 2.3.10
- box2d-kengz: 2.3.3
- numpy: 2.1.3
2. 🫙 构建缓存区
class Memory:
def __init__(self):
# 缓存区，用于存储当前一个 episode 中的经验
self.actions = []
self.states = []
self.logprobs = []
self.rewards = []
self.is_terminals = []
def clear_memory(self):
# 清空缓存
del self.actions[:]
del self.states[:]
del self.logprobs[:]
del self.rewards[:]
del self.is_terminals[:]
3. 🧱 构建 A2C 策略网络
class ActorCritic(nn.Module):
def __init__(self, state_dim, action_dim, n_latent_var):
super(ActorCritic, self).__init__()
# Actor 网络：
self.action_layer = nn.Sequential(
nn.Linear(state_dim, n_latent_var),
nn.Tanh(), # 你问我为什么不用其他激活函数？我也不清楚，待会儿查一下 QWQ
nn.Linear(n_latent_var, n_latent_var),
nn.Tanh(),
nn.Linear(n_latent_var, action_dim),
nn.Softmax(dim=-1) # 这里 dim=-1 相当于 dim=2，对 actions 进行归一化处理
)
# Critic 网络：
self.value_layer = nn.Sequential(
nn.Linear(state_dim, n_latent_var),
nn.Tanh(),
nn.Linear(n_latent_var, n_latent_var),
nn.Tanh(),
nn.Linear(n_latent_var, 1)
)
def forward(self):
# 你问这里为什么用不到 forward？大笨蛋！这里有两个网络你怎么前向传播？
raise NotImplementedError
def act(self, state, memory):
# 根据当前状态 s 生成下一步动作 a
state = torch.from_numpy(state).float().to(device)
actions_probs = self.action_layer(state)
dist = Categorical(actions_probs)
action = dist.sample()
# 压入缓存区
memory.states.append(state)
memory.actions.append(action)
memory.logprobs.append(dist.log_prob(action))
return action.item()
def evaluate(self, state, action):
# 评估模式
action_probs = self.action_layer(state)
dist = Categorical(action_probs)
action_logprobs = dist.log_prob(action)
dist_entropy = dist.entropy()
state_value = self.value_layer(state)
return action_logprobs, torch.squeeze(state_value), dist_entropy
ActorCritic 类是 PPO 算法的核心，它由两个部分组成：
- Actor：用于根据当前状态选择动作。通过一个神经网络来预测动作的概率分布，并通过
Categorical类来抽样动作。 - Critic：用于评估当前状态的价值，通过一个神经网络输出状态的价值函数。
act 方法接受当前状态，计算出动作概率分布并根据其进行抽样，保存当前的状态、动作和对应的 log 概率。
evaluate 方法用于计算动作的 log 概率、状态值和分布的熵。
4. 👷 构建 PPO 算法
class PPO:
def __init__(self, state_dim, action_dim, n_latent_var, lr, betas, gamma, K_epochs, eps_clip):
self.lr = lr
self.betas = betas
self.gamma = gamma
self.eps_clip = eps_clip
self.K_epochs = K_epochs
# 创建策略网络
self.policy = ActorCritic(state_dim, action_dim, n_latent_var).to(device)
self.optimizer = torch.optim.Adam(self.policy.parameters(), lr=lr, betas=betas)
# 这里使用 off policy 思想，给我们的 policy 召唤一个分身
self.policy_old = ActorCritic(state_dim, action_dim, n_latent_var).to(device)
self.policy_old.load_state_dict(self.policy.state_dict)
self.MseLoss = nn.MSELoss()
def update(self, memory):
# 利用蒙特卡洛算法计算 state rewards:
rewards = []
discounted_reward = 0
for reward, is_terminal in zip(reversed(memory.rewards), reversed(memory.is_terminal)):
if is_terminal:
discounted_reward = 0
discounted_reward = reward + self.gamma * discounted_reward
rewards.insert(0, discounted_reward)
# 归一化 rewards
rewards = torch.tensor(rewards, dtype=torch.float32).to(device)
rewards = (rewards - rewards.mean()) / (rewards.std() + 1e-5)
# 把 list 全部转换为 tensor
old_states = torch.stack(memory.states).to(device).detach()
old_actions = torch.stack(memory.actions).to(device).detach()
old_logprobs = torch.stack(memory.logprobs).to(device).detach()
# 优化策略
for _ in range(self.K_epochs):
# 评估 old actions 和 values
logprobs, state_values, dist_entropy = self.policy.evaluate(old_state, old_actions)
# 计算 ratio (pi_theta / pi_theta_old)
ratios = torch.exp(logprobs - old_logprobs.detach())
# 计算 surrogate loss
advantages = rewards - state_values.detach()
surr1 = ratios * advantages
surr2 = torch.clamp(ratios, 1.0-self.eps_clip, 1.0+self.eps_clip) * advantages
loss = -torch.min(surr1, surr2) + 0.5*self.MseLoss(state_values, rewards) - 0.01*dist_entropy
# 更新权重
self.optimizer.zero_grad()
loss.mean().backward()
self.optimizer.step()
# 将新的权重赋给我们的 old_policy
self.policy_old.load_state_dict(self.policy.state_dict())
PPO 类封装了强化学习训练的主要逻辑。update 方法通过经验回放更新策略：
- 首先，计算蒙特卡洛奖励。
- 然后对奖励归一化处理。
- 接着，通过多次优化（K-epochs），计算损失函数并进行梯度更新。
- 最后，将新的策略参数复制到旧策略中。
损失函数由三部分组成：
- Surrogate Loss：确保新的策略和旧的策略之间的比率 ratio 不便，以避免过大的策略变化。
- Value Loss：通过均方误差来最小化状态价值的误差。
- Entropy：通过熵来避免策略过于确定，保持探索性。
5. 🀄 主函数
def main():
# 创建环境
env_name = "LunarLander-V2"
env = gym.make(env_name, render_name="human")
#############################################################################
# 初始化参数
state_dim = env.observation_space.shape[0] # state_dim = 8
action_dim = 4 # 上下左右
render = True # 是否展示（建议设置为 False,这样可以加快训练速度）
solved_reward = 230 # 训练成功的指标
log_interval = 20
max_episodes = 50000
max_timesteps = 300
n_latent_var = 64
update_timestep = 2000
lr = 0.002
betas = (0.9, 0.999)
gamma = 0.99
K_epochs = 4
eps_clip = 0.2
random_seed = None
#############################################################################
if random_seed:
torch.manual_seed(random_seed)
env.seed(random_seed)
memory = Memory()
ppo = PPO(state_dim, action_dim, n_latent_var, lr, betas, gamma, K_epochs, eps_clip)
running_reward = 0
avg_length = 0
timestep = 0
for i_episode in range(1, max_episodes+1):
state = env.reset()
state = np.array(state[0])
for t in range(max_timesteps):
timestep += 1
action = ppo.policy_old.act(state, memory)
state, reward, done, _, _ = env.step(action)
# 注意：新版的 gym 在 env.step 时会返回 5 个值，具体请查阅官方文档或源码注释
memory.rewards.append(reward)
memory.is_terminals.append(done)
if timestep % update_timestep == 0:
ppo.update(memory)
memory.clear_memory()
timestep = 0
running_reward += reward
if render:
env.render()
if done:
break
avg_length += t
if running_reward > (log_interval*solved_reward):
print("#################### Solved ! ####################")
torch.save(ppo.policy.state_dict(), './PPO_{}.pth'.format(env.name))
break
if i_episode % log_interval == 0:
avg_length = int(avg_length/log_interval)
running_reward = int(running_reward/log_interval)
print("Episode {} \t avg length: {} \t reward:{}".format(i_episode, avg_length, running_reward))
running_reward = 0
avg_length = 0
6. 🚗 加载模型文件展示效果：
这里要新建一个
ppo_show.py文件
from ppo import PPO, Memory, ActorCritic
import gym
import torch
import numpy as np
env_name = "LunarLander-v2"
env = gym.make(env_name, render_mode="human")
state_dim = env.observation_space.shape[0]
action_dim = 4
render = True
max_timesteps = 300
n_latent_var = 64
lr = 0.0007
betas = (0.9, 0.999)
gamma = 0.99
K_epochs = 4
eps_clip = 0.2
n_episodes = 3
filename = "PPO_LunarLander-v2.pth"
directory = "./"
memory = Memory()
ppo = PPO(state_dim, action_dim, n_latent_var, lr, betas, gamma, K_epochs, eps_clip=eps_clip)
ppo.policy.load_state_dict(torch.load(filename))
ppo.policy_old.load_state_dict(torch.load(filename))
# logging variables
running_reward = 0
avg_length = 0
timestep = 0
# training loop
for i_episode in range(1, 20):
state = env.reset()
state = np.array(state[0])
for t in range(max_timesteps):
timestep += 1
action = ppo.policy_old.act(state, memory)
state, reward, done, _, _ = env.step(action)
memory.rewards.append(reward)
memory.is_terminals.append(done)
running_reward += reward
if render:
env.render()
if done:
break
avg_length += t
如有报错且找不到报错原因的，可以私信小生 🥺