我想学习深度强化学习从一个实际例子讲解一下环境配置、工程创建、运行等

环节。

环境配置：

安装 Python3
安装 PyTorch
安装 Gym

工程创建：

创建一个新的 Python 3 项目
在项目中创建一个名为“deep_rl”的文件夹
在“deep_rl”文件夹中创建一个名为“agent.py”的文件，用于编写强化学习代理
在“deep_rl”文件夹中创建一个名为“environment.py”的文件，用于编写强化学习环境
在“deep_rl”文件夹中创建一个名为“main.py”的文件，用于运行强化学习实验

运行：

在“agent.py”中编写强化学习代理代码
在“environment.py”中编写强化学习环境代码
在“main.py”中实例化代理和环境，并运行强化学习实验

示例代码：

agent.py

import torch
import torch.nn as nn
import torch.optim as optim

class DQNAgent():
    def __init__(self, state_size, action_size):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.state_size = state_size
        self.action_size = action_size
        self.q_network = nn.Sequential(
            nn.Linear(state_size, 256),
            nn.ReLU(),
            nn.Linear(256, 256),
            nn.ReLU(),
            nn.Linear(256, action_size)
        ).to(self.device)
        self.optimizer = optim.Adam(self.q_network.parameters(), lr=0.001)

    def get_action(self, state):
        state = torch.FloatTensor(state).to(self.device)
        q_values = self.q_network(state)
        action = q_values.argmax().detach().cpu().item()
        return action

    def train(self, states, actions, rewards, next_states, dones):
        states = torch.FloatTensor(states).to(self.device)
        actions = torch.LongTensor(actions).to(self.device)
        rewards = torch.FloatTensor(rewards).to(self.device)
        next_states = torch.FloatTensor(next_states).to(self.device)
        dones = torch.FloatTensor(dones).to(self.device)
        q_values = self.q_network(states).gather(1, actions.unsqueeze(1)).squeeze(1)
        next_q_values = self.q_network(next_states).max(1)[0].detach()
        expected_q_values = rewards + (1 - dones) * 0.99 * next_q_values
        loss = nn.MSELoss()(q_values, expected_q_values)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

environment.py

import gym

class CartPoleEnvironment():
    def __init__(self):
        self.env = gym.make('CartPole-v0')
        self.state_size = self.env.observation_space.shape[0]
        self.action_size = self.env.action_space.n

    def reset(self):
        return self.env.reset()

    def step(self, action):
        next_state, reward, done, _ = self.env.step(action)
        return next_state, reward, done

main.py

from agent import DQNAgent
from environment import CartPoleEnvironment

env = CartPoleEnvironment()
agent = DQNAgent(env.state_size, env.action_size)

for episode in range(100):
    state = env.reset()
    done = False
    total_reward = 0
    while not done:
        action = agent.get_action(state)
        next_state, reward, done = env.step(action)
        total_reward += reward
        agent.train(state, action, reward, next_state, done)
        state = next_state
    print(f"Episode {episode}: Total Reward = {total_reward}")
``