建网赌网站流程,海口网站开发建设,宁波seo网络推广代理公司,广州建工设计院gym版本是0.26.1 CartPole-v1的详细信息#xff0c;点链接里看就行了。 修改了下动手深度强化学习对应的代码。 然后这里 J ( θ ) J(\theta) J(θ)梯度上升更新的公式是用的不严谨的#xff0c;这个和王树森书里讲的严谨公式有点区别。 代码
import gym
import torch
from …gym版本是0.26.1 CartPole-v1的详细信息点链接里看就行了。 修改了下动手深度强化学习对应的代码。 然后这里 J ( θ ) J(\theta) J(θ)梯度上升更新的公式是用的不严谨的这个和王树森书里讲的严谨公式有点区别。 代码
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 tqdm import tqdm
import rl_utils # 这个要下载源码然后放到同个文件目录下,链接在上面给出了
from d2l import torch as d2l # 这个是动手深度学习的库, pip/conda install d2l 就好了class 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),dim1)class REINFORCE:def __init__(self, state_dim, hidden_dim, action_dim, learning_rate, gamma, device):self.policy_net PolicyNet(state_dim, hidden_dim, action_dim).to(device)self.optimizer torch.optim.Adam(self.policy_net.parameters(), lr learning_rate)self.gamma gamma # 折扣因子self.device devicedef take_action(self, state): # 根据动作概率分布随机采样state torch.tensor(np.array([state]),dtypetorch.float).to(self.device)probs self.policy_net(state)action_dist torch.distributions.Categorical(probs)action action_dist.sample()return action.item()def update(self, transition_dict): # 公式用的是简化推导reward_list transition_dict[rewards]state_list transition_dict[states]action_list transition_dict[actions]G 0self.optimizer.zero_grad()for i in reversed(range(len(reward_list))): # 从最后一步算起reward reward_list[i]state torch.tensor(np.array([state_list[i]]), dtypetorch.float).to(self.device)action torch.tensor([action_list[i]]).reshape(-1,1).to(self.device)log_prob torch.log(self.policy_net(state).gather(1, action))G self.gamma * G reward loss -log_prob * G # 因为梯度更新是减的所以取个负号loss.backward()self.optimizer.step()
lr 1e-3
num_episodes 1000
hidden_dim 128
gamma 0.98
device d2l.try_gpu()env_nameCartPole-v1
env gym.make(env_name)
print(f_max_episode_steps:{env._max_episode_steps})
torch.manual_seed(0)
state_dim env.observation_space.shape[0]
action_dim env.action_space.nagent REINFORCE(state_dim, hidden_dim, action_dim, lr, gamma, device)
return_list []
for i in range(10):with tqdm(totalint(num_episodes/10), descfIteration {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 : # 主要是这部分和原始的有点不同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_episode1) % 10 0:pbar.set_postfix({episode: %d % (num_episodes / 10 * i i_episode1), return: %.3f % np.mean(return_list[-10:])})pbar.update(1)episodes_list list(range(len(return_list)))
plt.plot(episodes_list, return_list)
plt.xlabel(Episodes)
plt.ylabel(Returns)
plt.title(fREINFORCE 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(Returns)
plt.title(fREINFORCE on {env_name})
plt.show()我是在jupyter里直接跑的结果如下所示。
