PyTorch强化学习实战：从零到精通
PyTorch 强化学习 Demo：从理论到实践
强化学习（Reinforcement Learning, RL）是机器学习的一个重要分支，通过与环境的交互学习最优策略。PyTorch 作为深度学习框架，因其动态计算图和易用性，成为实现强化学习算法的热门选择。以下是一个基于 PyTorch 的强化学习 Demo，涵盖环境搭建、算法实现和训练流程。
环境搭建
选择 OpenAI Gym 作为实验环境，它提供了多种标准化的强化学习任务。安装 Gym 和 PyTorch：
pip install gym torch
以 CartPole 任务为例，目标是控制小车保持平衡，使杆子不倒。创建环境：
import gym
env = gym.make('CartPole-v1')
Q-Learning 算法实现
Q-Learning 是一种经典的强化学习算法，通过更新 Q 值表来学习最优策略。以下是 PyTorch 实现的核心部分：
import torch
import torch.nn as nn
import torch.optim as optim
class QNetwork(nn.Module):
def __init__(self, state_size, action_size):
super(QNetwork, self).__init__()
self.fc1 = nn.Linear(state_size, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, action_size)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
return self.fc3(x)
经验回放机制
强化学习通常需要经验回放（Experience Replay）来提高样本利用率。实现一个简单的回放缓冲区：
import random
from collections import deque
class ReplayBuffer:
def __init__(self, capacity):
self.buffer = deque(maxlen=capacity)
def push(self, state, action, reward, next_state, done):
self.buffer.append((state, action, reward, next_state, done))
def sample(self, batch_size):
return random.sample(self.buffer, batch_size)
def __len__(self):
return len(self.buffer)
训练流程
将上述组件整合为完整的训练流程，包括环境交互、模型更新和策略优化：
def train(env, model, target_model, optimizer, buffer, batch_size=32, gamma=0.99):
state = env.reset()
episode_reward = 0
for t in range(1000):
state_tensor = torch.FloatTensor(state).unsqueeze(0)
q_values = model(state_tensor)
action = q_values.argmax().item()
next_state, reward, done, _ = env.step(action)
buffer.push(state, action, reward, next_state, done)
episode_reward += reward
if len(buffer) >= batch_size:
transitions = buffer.sample(batch_size)
batch = list(zip(*transitions))
states = torch.FloatTensor(batch[0])
actions = torch.LongTensor(batch[1])
rewards = torch.FloatTensor(batch[2])
next_states = torch.FloatTensor(batch[3])
dones = torch.FloatTensor(batch[4])
current_q = model(states).gather(1, actions.unsqueeze(1))
next_q = target_model(next_states).max(1)[0].detach()
target_q = rewards + gamma * next_q * (1 - dones)
loss = nn.MSELoss()(current_q.squeeze(), target_q)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if done:
break
return episode_reward
模型评估与改进
训练完成后，评估模型性能并考虑以下改进方向：
- 使用 Double DQN 减少过估计问题
- 引入 Prioritized Experience Replay 提高关键样本的利用率
- 尝试 Dueling DQN 架构分离状态价值和优势函数
可视化训练过程
通过 Matplotlib 绘制训练过程中的奖励曲线，直观展示模型的学习效果：
import matplotlib.pyplot as plt
rewards = []
for episode in range(100):
reward = train(env, model, target_model, optimizer, buffer)
rewards.append(reward)
plt.plot(rewards)
plt.xlabel('Episode')
plt.ylabel('Reward')
plt.show()
这个 Demo 展示了 PyTorch 在强化学习中的应用，从环境交互到算法实现，涵盖了关键组件。通过调整网络结构、超参数和训练策略，可以进一步优化性能。
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