动手学强化学习 第 15 章 模仿学习 训练代码

基于 https://github.com/boyu-ai/Hands-on-RL/blob/main/%E7%AC%AC15%E7%AB%A0-%E6%A8%A1%E4%BB%BF%E5%AD%A6%E4%B9%A0.ipynb

理论 模仿学习

修改了警告和报错

运行环境

Debian GNU/Linux 12
Python 3.9.19
torch 2.0.1
gym 0.26.2

运行代码

#!/usr/bin/env pythonimport gym
import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import random
import rl_utilsclass PolicyNet(torch.nn.Module):def __init__(self, state_dim, hidden_dim, action_dim):super(PolicyNet, self).__init__()self.fc1 = torch.nn.Linear(state_dim, hidden_dim)self.fc2 = torch.nn.Linear(hidden_dim, action_dim)def forward(self, x):x = F.relu(self.fc1(x))return F.softmax(self.fc2(x), dim=1)class ValueNet(torch.nn.Module):def __init__(self, state_dim, hidden_dim):super(ValueNet, self).__init__()self.fc1 = torch.nn.Linear(state_dim, hidden_dim)self.fc2 = torch.nn.Linear(hidden_dim, 1)def forward(self, x):x = F.relu(self.fc1(x))return self.fc2(x)class PPO:''' PPO算法,采用截断方式 '''def __init__(self, state_dim, hidden_dim, action_dim, actor_lr, critic_lr,lmbda, epochs, eps, gamma, device):self.actor = PolicyNet(state_dim, hidden_dim, action_dim).to(device)self.critic = ValueNet(state_dim, hidden_dim).to(device)self.actor_optimizer = torch.optim.Adam(self.actor.parameters(),lr=actor_lr)self.critic_optimizer = torch.optim.Adam(self.critic.parameters(),lr=critic_lr)self.gamma = gammaself.lmbda = lmbdaself.epochs = epochs  # 一条序列的数据用于训练轮数self.eps = eps  # PPO中截断范围的参数self.device = devicedef take_action(self, state):state = torch.tensor(np.array([state]), dtype=torch.float).to(self.device)probs = self.actor(state)action_dist = torch.distributions.Categorical(probs)action = action_dist.sample()return action.item()def update(self, transition_dict):states = torch.tensor(np.array(transition_dict['states']),dtype=torch.float).to(self.device)actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(self.device)rewards = torch.tensor(transition_dict['rewards'],dtype=torch.float).view(-1, 1).to(self.device)next_states = torch.tensor(np.array(transition_dict['next_states']),dtype=torch.float).to(self.device)dones = torch.tensor(transition_dict['dones'],dtype=torch.float).view(-1, 1).to(self.device)td_target = rewards + self.gamma * self.critic(next_states) * (1 -dones)td_delta = td_target - self.critic(states)advantage = rl_utils.compute_advantage(self.gamma, self.lmbda,td_delta.cpu()).to(self.device)old_log_probs = torch.log(self.actor(states).gather(1,actions)).detach()for _ in range(self.epochs):log_probs = torch.log(self.actor(states).gather(1, actions))ratio = torch.exp(log_probs - old_log_probs)surr1 = ratio * advantagesurr2 = torch.clamp(ratio, 1 - self.eps,1 + self.eps) * advantage  # 截断actor_loss = torch.mean(-torch.min(surr1, surr2))  # PPO损失函数critic_loss = torch.mean(F.mse_loss(self.critic(states), td_target.detach()))self.actor_optimizer.zero_grad()self.critic_optimizer.zero_grad()actor_loss.backward()critic_loss.backward()self.actor_optimizer.step()self.critic_optimizer.step()actor_lr = 1e-3
critic_lr = 1e-2
num_episodes = 250
hidden_dim = 128
gamma = 0.98
lmbda = 0.95
epochs = 10
eps = 0.2
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")env_name = 'CartPole-v1'
env = gym.make(env_name)
env.reset(seed=0)
torch.manual_seed(0)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
ppo_agent = PPO(state_dim, hidden_dim, action_dim, actor_lr, critic_lr, lmbda,epochs, eps, gamma, device)return_list = rl_utils.train_on_policy_agent(env, ppo_agent, num_episodes)def sample_expert_data(n_episode):states = []actions = []for episode in range(n_episode):state = env.reset()[0]done = Falsewhile not done and len(states) < 10000:action = ppo_agent.take_action(state)states.append(state)actions.append(action)next_state, reward, done, _, __ = env.step(action)state = next_statereturn np.array(states), np.array(actions)env.reset(seed=0)
torch.manual_seed(0)
random.seed(0)
n_episode = 1
expert_s, expert_a = sample_expert_data(n_episode)n_samples = 30  # 采样30个数据
random_index = random.sample(range(expert_s.shape[0]), n_samples)
expert_s = expert_s[random_index]
expert_a = expert_a[random_index]class BehaviorClone:def __init__(self, state_dim, hidden_dim, action_dim, lr):self.policy = PolicyNet(state_dim, hidden_dim, action_dim).to(device)self.optimizer = torch.optim.Adam(self.policy.parameters(), lr=lr)def learn(self, states, actions):states = torch.tensor(states, dtype=torch.float).to(device)actions = torch.tensor(actions).view(-1, 1).to(device)log_probs = torch.log(self.policy(states).gather(1, actions))bc_loss = torch.mean(-log_probs)  # 最大似然估计self.optimizer.zero_grad()bc_loss.backward()self.optimizer.step()def take_action(self, state):state = torch.tensor(np.array([state]), dtype=torch.float).to(device)probs = self.policy(state)action_dist = torch.distributions.Categorical(probs)action = action_dist.sample()return action.item()def test_agent(agent, env, n_episode):return_list = []for episode in range(n_episode):episode_return = 0state = env.reset()[0]done = Falsewhile not done:action = agent.take_action(state)next_state, reward, done, _, __ = env.step(action)state = next_stateepisode_return += rewardreturn_list.append(episode_return)return np.mean(return_list)env.reset(seed=0)
torch.manual_seed(0)
np.random.seed(0)lr = 1e-3
bc_agent = BehaviorClone(state_dim, hidden_dim, action_dim, lr)
n_iterations = 1000
batch_size = 64
test_returns = []with tqdm(total=n_iterations, desc="进度条") as pbar:for i in range(n_iterations):sample_indices = np.random.randint(low=0,high=expert_s.shape[0],size=batch_size)bc_agent.learn(expert_s[sample_indices], expert_a[sample_indices])current_return = test_agent(bc_agent, env, 5)test_returns.append(current_return)if (i + 1) % 10 == 0:pbar.set_postfix({'return': '%.3f' % np.mean(test_returns[-10:])})pbar.update(1)iteration_list = list(range(len(test_returns)))
plt.plot(iteration_list, test_returns)
plt.xlabel('Iterations')
plt.ylabel('Returns')
plt.title('BC on {}'.format(env_name))
plt.show()class Discriminator(nn.Module):def __init__(self, state_dim, hidden_dim, action_dim):super(Discriminator, self).__init__()self.fc1 = torch.nn.Linear(state_dim + action_dim, hidden_dim)self.fc2 = torch.nn.Linear(hidden_dim, 1)def forward(self, x, a):cat = torch.cat([x, a], dim=1)x = F.relu(self.fc1(cat))return torch.sigmoid(self.fc2(x))class GAIL:def __init__(self, agent, state_dim, action_dim, hidden_dim, lr_d):self.discriminator = Discriminator(state_dim, hidden_dim,action_dim).to(device)self.discriminator_optimizer = torch.optim.Adam(self.discriminator.parameters(), lr=lr_d)self.agent = agentdef learn(self, expert_s, expert_a, agent_s, agent_a, next_s, dones):expert_states = torch.tensor(expert_s, dtype=torch.float).to(device)expert_actions = torch.tensor(expert_a).to(device)agent_states = torch.tensor(np.array(agent_s), dtype=torch.float).to(device)agent_actions = torch.tensor(agent_a).to(device)expert_actions = F.one_hot(expert_actions, num_classes=2).float()agent_actions = F.one_hot(agent_actions, num_classes=2).float()expert_prob = self.discriminator(expert_states, expert_actions)agent_prob = self.discriminator(agent_states, agent_actions)discriminator_loss = nn.BCELoss()(agent_prob, torch.ones_like(agent_prob)) + nn.BCELoss()(expert_prob, torch.zeros_like(expert_prob))self.discriminator_optimizer.zero_grad()discriminator_loss.backward()self.discriminator_optimizer.step()rewards = -torch.log(agent_prob).detach().cpu().numpy()transition_dict = {'states': agent_s,'actions': agent_a,'rewards': rewards,'next_states': next_s,'dones': dones}self.agent.update(transition_dict)env.reset(seed=0)
torch.manual_seed(0)
lr_d = 1e-3
agent = PPO(state_dim, hidden_dim, action_dim, actor_lr, critic_lr, lmbda,epochs, eps, gamma, device)
gail = GAIL(agent, state_dim, action_dim, hidden_dim, lr_d)
n_episode = 500
return_list = []with tqdm(total=n_episode, desc="进度条") as pbar:for i in range(n_episode):episode_return = 0state = env.reset()[0]done = Falsestate_list = []action_list = []next_state_list = []done_list = []while not done and len(state_list) < 10000:action = agent.take_action(state)next_state, reward, done, _, __ = env.step(action)state_list.append(state)action_list.append(action)next_state_list.append(next_state)done_list.append(done)state = next_stateepisode_return += rewardreturn_list.append(episode_return)gail.learn(expert_s, expert_a, state_list, action_list,next_state_list, done_list)if (i + 1) % 10 == 0:pbar.set_postfix({'return': '%.3f' % np.mean(return_list[-10:])})pbar.update(1)iteration_list = list(range(len(return_list)))
plt.plot(iteration_list, return_list)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title('GAIL on {}'.format(env_name))
plt.show()

rl_utils.py

参考 动手学强化学习 第 14 章 SAC 算法 训练代码-CSDN博客