强化学习之REINFORECE策略梯度算法——已CartPole环境为例

整体代码如下：

import gym
import numpy as np
import torch
import matplotlib.pyplot as plt
from tqdm import tqdm
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
def moving_average(a, window_size):cumulative_sum = np.cumsum(np.insert(a, 0, 0)) middle = (cumulative_sum[window_size:] - cumulative_sum[:-window_size]) / window_sizer = np.arange(1, window_size-1, 2)begin = np.cumsum(a[:window_size-1])[::2] / rend = (np.cumsum(a[:-window_size:-1])[::2] / r)[::-1]return np.concatenate((begin, middle, end))
class PolicyNetwork(torch.nn.Module):def __init__(self,statedim,hiddendim,actiondim):super(PolicyNetwork,self).__init__()self.cf1=torch.nn.Linear(statedim,hiddendim)self.cf2=torch.nn.Linear(hiddendim,actiondim)def forward(self,x):x=torch.nn.functional.relu(self.cf1(x))return torch.nn.functional.softmax(self.cf2(x),dim=1)
class REINFORCE:def __init__(self,statedim,hiddendim,actiondim,learningrate,gamma,device):self.policynet=PolicyNetwork(statedim,hiddendim,actiondim).to(device)self.gamma=gammaself.device=deviceself.optimizer=torch.optim.Adam(self.policynet.parameters(),lr=learningrate)def takeaction(self,state):state=torch.tensor([state],dtype=torch.float).to(self.device)probs=self.policynet(state)actiondist=torch.distributions.Categorical(probs)#torch.distributions.Categorical：这是 PyTorch 中用于表示类别分布的类,可以使用 actiondist.sample() 方法从这个分布中随机采样一个类别action=actiondist.sample()return action.item()def update(self,transitiondist):statelist=transitiondist['states']rewardlist=transitiondist['rewards']actionlist=transitiondist['actions']G=0self.optimizer.zero_grad()for i in reversed(range(len(rewardlist))):#从最后一步计算起reward=rewardlist[i]state=statelist[i]action=actionlist[i]state=torch.tensor([state],dtype=torch.float).to(self.device)action=torch.tensor([action]).view(-1,1).to(self.device)logprob=torch.log(self.policynet(state).gather(1,action)) #.gather(1, action) 方法从策略网络的输出中提取对应于特定动作 action 的概率值。这里的 1 表示沿着维度 1（通常对应于动作维度）进行索引。G=self.gamma*G+rewardloss=-logprob*G#每一步的损失函数loss.backward()#反向传播计算梯度self.optimizer.step()#更新参数,梯度下降learningrate=4e-3
episodesnum=1000
hiddendim=128
gamma=0.99
pbarnum=10
printreturnnum=10
device=torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
env=gym.make('CartPole-v1')
env.reset(seed=880)
torch.manual_seed(880)
statedim=env.observation_space.shape[0]
actiondim=env.action_space.n
agent=REINFORCE(statedim=statedim,hiddendim=hiddendim,actiondim=actiondim,learningrate=learningrate,gamma=gamma,device=device)
returnlist=[]
for  k in range(pbarnum):with tqdm(total=int(episodesnum/pbarnum),desc='Iteration %d'%k)as pbar:for episode in range(int(episodesnum/pbarnum)):g=0transitiondist={'states':[],'actions':[],'nextstates':[],'rewards':[]}state,_=env.reset(seed=880)done=Falsewhile not done:action=agent.takeaction(state)nextstate,reward,done,truncated,_=env.step(action)done=done or truncatedtransitiondist['states'].append(state)transitiondist['actions'].append(action)transitiondist['nextstates'].append(nextstate)transitiondist['rewards'].append(reward)state=nextstateg=g+rewardreturnlist.append(g)agent.update(transitiondist)if (episode+1)%(printreturnnum)==0:pbar.set_postfix({'Episode':'%d'%(episodesnum//pbarnum+episode+1),'Return':'%.3f'%np.mean(returnlist[-printreturnnum:])})pbar.update(1)episodelist=list(range(len(returnlist)))
plt.plot(episodelist,returnlist)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title('REINFORCE on {}'.format(env.spec.name))     
plt.show()
mvreturn=moving_average(returnlist,9)
plt.plot(episodelist,mvreturn)
plt.xlabel('Episodes')
plt.ylabel('Returns')
plt.title('REINFORCE on {}'.format(env.spec.name))
plt.show()  

效果：