Actor-Critic 跑 CartPole-v1

本文主要是介绍Actor-Critic 跑 CartPole-v1，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

gym-0.26.1
CartPole-v1
Actor-Critic

这里采用时序差分残差
$\psi_t = r_t + \gamma V_{\pi _ \theta} (s_{t+1}) - V_{\pi _ \theta}({s_t})$
详细请参考动手学强化学习
简单来说就是 reforce 是采用蒙特卡洛搜索方法来估计Q(s,a) ,然后这里先是把状态价值函数V作为基线, 然后利用Q = r + gamma * V 得到上式。

代码如下

import gym
import torch
from torch import nn
from torch.nn import functional as F
import numpy as np
import matplotlib.pyplot as plt
from d2l import torch as d2l
import rl_utils
from tqdm import tqdmclass PolicyNet(nn.Module):def __init__(self, state_dim, hidden_dim, action_dim):super().__init__()self.fc1 = nn.Linear(state_dim, hidden_dim)self.fc2 = 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(nn.Module):def __init__(self, state_dim, hidden_dim):super().__init__()self.fc1 = nn.Linear(state_dim, hidden_dim)self.fc2 = nn.Linear(hidden_dim, 1)def forward(self, X):X = F.relu(self.fc1(X))return self.fc2(X)class ActorCritic:def __init__(self, state_dim, hidden_dim, action_dim, actor_lr, critic_lr, gamma, device):# 策略网络self.actor = PolicyNet(state_dim, hidden_dim, action_dim).to(device)# 价值网络self.critic = ValueNet(state_dim, hidden_dim).to(device)# 策略网络优化器self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr = actor_lr)#价值网络优化器self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr = critic_lr)self.gamma = gammaself.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(transition_dict['states'], dtype=torch.float).to(self.device)actions = torch.tensor(transition_dict['actions']).reshape(-1,1).to(self.device)rewards = torch.tensor(transition_dict['rewards']).reshape(-1,1).to(device)next_states = torch.tensor(transition_dict['next_states'], dtype=torch.float).to(self.device)dones = torch.tensor(transition_dict['dones'], dtype=torch.float).reshape(-1,1).to(self.device)# 时分差分目标td_target = rewards + self.gamma * self.critic(next_states) * (1- dones)# 时分差序目标td_delta = td_target - self.critic(states)log_probs = torch.log(self.actor(states).gather(1, actions))actor_loss = torch.mean(-log_probs * td_delta.detach())# 均方误差critic_loss= torch.mean(F.mse_loss(self.critic(states), td_target.detach()))self.actor_optimizer.zero_grad()self.critic_optimizer.zero_grad()# 计算策略网络的梯度actor_loss.backward()# 计算价值网络的梯度critic_loss.backward()# 更新策略网络梯度self.actor_optimizer.step()# 跟新价值网络梯度self.critic_optimizer.step()def train(env, agent, num_episodes):return_list = []for i in range(10):with tqdm(total=int(num_episodes/10), desc='Iteration %d' % i) as pbar:for i_episode in range(int(num_episodes/10)):episode_return = 0transition_dict = {'states': [], 'actions': [], 'next_states': [], 'rewards': [], 'dones': []}state = env.reset()[0]done ,truncated = False, Falsewhile not done and not truncated:action = agent.take_action(state)next_state, reward, done, truncated, info = env.step(action)transition_dict['states'].append(state)transition_dict['actions'].append(action)transition_dict['next_states'].append(next_state)transition_dict['rewards'].append(reward)transition_dict['dones'].append(done)state = next_stateepisode_return += rewardreturn_list.append(episode_return)agent.update(transition_dict)if (i_episode+1) % 10 == 0:pbar.set_postfix({'episode': '%d' % (num_episodes/10 * i + i_episode+1), 'return': '%.3f' % np.mean(return_list[-10:])})pbar.update(1)return return_list

actor_lr = 1e-3
critic_lr = 1e-2
num_episodes = 1000
hidden_dim = 128
gamma = 0.98
device = d2l.try_gpu()env_name = 'CartPole-v1'
env = gym.make(env_name)
torch.manual_seed(0)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
agent = ActorCritic(state_dim, hidden_dim, action_dim, actor_lr, critic_lr, gamma, device)return_list = train(env, agent, num_episodes)
episodes_list = list(range(len(return_list)))
plt.plot(episodes_list, return_list)
plt.xlabel('Episodes')
plt.ylabel('Return')
plt.title(f'Actor-Critic on {env_name}')
plt.show()mv_return = rl_utils.moving_average(return_list, 9)
plt.plot(episodes_list, mv_return)
plt.xlabel('Episodes')
plt.ylabel('Return')
plt.title(f'Actor-Critic on {env_name}')
plt.show()