A3C——pytorch

这一部分的代码都来自莫凡,由于所看的书《白话强化学习》里面的代码块有一部分看不懂,转而看其他老师的代码,感觉莫老师的代码通俗易懂,但是语法可能和书上所学的有所不一样,所以还是读了这个代码,作了点注释,以供之后翻阅
main.py

"""
Reinforcement Learning (A3C) using Pytroch + multiprocessing.
The most simple implementation for continuous action.

View more on my Chinese tutorial page [莫烦Python](https://morvanzhou.github.io/).
"""

import torch
import torch.nn as nn
from utils import v_wrap, set_init, push_and_pull, record
import torch.nn.functional as F
import torch.multiprocessing as mp
from shared_adam import SharedAdam
import gym
import math, os
os.environ["OMP_NUM_THREADS"] = "1"

UPDATE_GLOBAL_ITER = 5
GAMMA = 0.9
MAX_EP = 3000
MAX_EP_STEP = 200

env = gym.make('Pendulum-v0')
N_S = env.observation_space.shape[0]
# 环境观测空间
N_A = env.action_space.shape[0]
# 环境动作空间


class Net(nn.Module):
    def __init__(self, s_dim, a_dim):
        super(Net, self).__init__()
        self.s_dim = s_dim
        self.a_dim = a_dim
        self.a1 = nn.Linear(s_dim, 200)
        self.mu = nn.Linear(200, a_dim)
        self.sigma = nn.Linear(200, a_dim)
        self.c1 = nn.Linear(s_dim, 100)
        self.v = nn.Linear(100, 1)
        set_init([self.a1, self.mu, self.sigma, self.c1, self.v])
        # 将参数初始化
        self.distribution = torch.distributions.Normal

    def forward(self, x):
        a1 = F.relu6(self.a1(x))
        mu = 2 * F.tanh(
### 基于PyTorch实现的A3C算法代码实例 #### A3C简介 异步优势演员评论家(Asynchronous Advantage Actor-Critic, A3C)是一种强化学习方法,它通过多个代理环境副本并行运行来加速训练过程。这种方法不仅提高了数据效率还增强了探索能力。 #### PyTorch中的A3C实现概述 为了利用GPU的强大计算能力和Python编程语言的优势,可以使用PyTorch框架来实现实验性的或研究级别的A3C模型。下面提供了一个简化版的A3C算法在PyTorch下的具体编码方式[^1]: ```python import torch import torch.nn as nn import torch.optim as optim from collections import namedtuple import multiprocessing as mp import gym Transition = namedtuple('Transition', ('state', 'action', 'next_state', 'reward')) class ActorCritic(nn.Module): def __init__(self, num_inputs, num_outputs, hidden_size=256): super(ActorCritic, self).__init__() self.critic = nn.Sequential( nn.Linear(num_inputs, hidden_size), nn.ReLU(), nn.Linear(hidden_size, 1)) self.actor = nn.Sequential( nn.Linear(num_inputs, hidden_size), nn.ReLU(), nn.Linear(hidden_size, num_outputs), nn.Softmax(dim=-1)) def forward(self, x): value = self.critic(x) probs = self.actor(x) dist = torch.distributions.Categorical(probs) return dist, value def compute_returns(next_value, rewards, masks, gamma=0.99): R = next_value returns = [] for step in reversed(range(len(rewards))): R = rewards[step] + gamma * R * masks[step] returns.insert(0, R) return returns # 初始化参数... num_processes = 16 env_name = "CartPole-v1" learning_rate = 3e-4 gamma = 0.99 tau = 1. device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = ActorCritic(env.observation_space.shape[0], env.action_space.n).to(device) optimizer = optim.Adam(model.parameters(), lr=learning_rate) # 定义工作函数用于多进程执行... def worker(): pass # 这里省略了具体的worker逻辑 if __name__ == '__main__': processes = [] for i in range(num_processes): p = mp.Process(target=worker, args=(i,)) p.start() processes.append(p) for p in processes: p.join() ``` 此段代码展示了如何创建一个简单的`ActorCritic`类来进行策略梯度更新,并定义了一些辅助功能如`compute_returns()`以帮助完成完整的A3C流程。需要注意的是,在实际应用中还需要编写适当的工作线程(worker),以便能够真正发挥出A3C算法的优点——即跨不同环境的同时模拟和学习[^2]。
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