强化学习教学 Pytorch 实现的9种 DRL 算法 包括以下9种：REINFORCE、Actor-Critic、Rainbow-DQN、PPO-discrete、PPO-continous、DDPG、TD3、SAC、PPO-discrete-RNN 非常适合强化学习初学者 环境要求： python==3.7.9 numpy==1.19.4 pytorch==1.12.0 tensorboard==0.6.0 gym==0.21.0

嘿，强化学习的初学者们！今天要给大家分享超棒的内容，使用Pytorch实现9种深度强化学习（DRL）算法，带你快速上手强化学习。

一、环境要求

在开始之前，先确保咱们的环境配置好。这里需要的版本如下：

• python==3.7.9：作为主流的Python版本，它的稳定性和兼容性都非常不错，适合进行各类开发，咱们的强化学习代码也不例外。
• numpy==1.19.4：这个强大的数学计算库，在处理矩阵运算、数组操作等方面效率极高，强化学习中大量的数据处理都离不开它。
• pytorch==1.12.0：本次实现算法的核心框架，PyTorch以其动态计算图和易于理解的代码结构受到广泛喜爱。
• tensorboard==0.6.0：用于可视化训练过程，让你清晰看到算法的学习曲线，了解训练进展。
• gym==0.21.0：OpenAI Gym，提供了丰富的环境供强化学习算法进行训练和测试，从简单的CartPole到复杂的Atari游戏环境都有。

二、9种DRL算法实现

1. REINFORCE

REINFORCE算法是基于策略梯度的算法。简单来说，它通过不断采样轨迹，根据奖励来调整策略网络的参数，使得智能体采取的行动能获得更高的奖励。

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

class Policy(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(Policy, self).__init__()
        self.fc1 = nn.Linear(state_dim, 128)
        self.fc2 = nn.Linear(128, action_dim)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        action_probs = torch.softmax(self.fc2(x), dim=1)
        return action_probs

def reinforce(env, policy, optimizer, num_episodes, gamma=0.99):
    rewards_all_episodes = []
    for episode in range(num_episodes):
        state = env.reset()
        state = torch.FloatTensor(state).unsqueeze(0)
        rewards_current_episode = []
        while True:
            action_probs = policy(state)
            dist = torch.distributions.Categorical(action_probs)
            action = dist.sample()
            next_state, reward, done, _ = env.step(action.item())
            next_state = torch.FloatTensor(next_state).unsqueeze(0)
            rewards_current_episode.append(reward)
            state = next_state
            if done:
                break
        rewards_all_episodes.append(sum(rewards_current_episode))
        returns = []
        G = 0
        for r in rewards_current_episode[::-1]:
            G = r + gamma * G
            returns.insert(0, G)
        returns = torch.tensor(returns)
        returns = (returns - returns.mean()) / (returns.std() + 1e-9)
        log_probs = []
        state = env.reset()
        state = torch.FloatTensor(state).unsqueeze(0)
        while True:
            action_probs = policy(state)
            dist = torch.distributions.Categorical(action_probs)
            action = dist.sample()
            log_probs.append(dist.log_prob(action))
            next_state, _, done, _ = env.step(action.item())
            next_state = torch.FloatTensor(next_state).unsqueeze(0)
            state = next_state
            if done:
                break
        log_probs = torch.stack(log_probs)
        loss = - (log_probs * returns).mean()
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    return rewards_all_episodes

2. Actor - Critic

Actor - Critic结合了策略梯度（Actor部分）和价值函数估计（Critic部分）。Actor负责选择行动，Critic负责评估Actor的行动价值。

class Actor(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(Actor, self).__init__()
        self.fc1 = nn.Linear(state_dim, 128)
        self.fc2 = nn.Linear(128, action_dim)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        action_probs = torch.softmax(self.fc2(x), dim=1)
        return action_probs

class Critic(nn.Module):
    def __init__(self, state_dim):
        super(Critic, self).__init__()
        self.fc1 = nn.Linear(state_dim, 128)
        self.fc2 = nn.Linear(128, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        value = self.fc2(x)
        return value

def actor_critic(env, actor, critic, actor_optimizer, critic_optimizer, num_episodes, gamma=0.99):
    rewards_all_episodes = []
    for episode in range(num_episodes):
        state = env.reset()
        state = torch.FloatTensor(state).unsqueeze(0)
        rewards_current_episode = []
        while True:
            action_probs = actor(state)
            dist = torch.distributions.Categorical(action_probs)
            action = dist.sample()
            next_state, reward, done, _ = env.step(action.item())
            next_state = torch.FloatTensor(next_state).unsqueeze(0)
            rewards_current_episode.append(reward)
            state = next_state
            if done:
                break
        rewards_all_episodes.append(sum(rewards_current_episode))
        returns = []
        G = 0
        for r in rewards_current_episode[::-1]:
            G = r + gamma * G
            returns.insert(0, G)
        returns = torch.tensor(returns)
        state = env.reset()
        state = torch.FloatTensor(state).unsqueeze(0)
        values = []
        while True:
            value = critic(state)
            values.append(value)
            action_probs = actor(state)
            dist = torch.distributions.Categorical(action_probs)
            action = dist.sample()
            next_state, _, done, _ = env.step(action.item())
            next_state = torch.FloatTensor(next_state).unsqueeze(0)
            state = next_state
            if done:
                break
        values = torch.cat(values)
        advantages = returns - values.detach()
        actor_loss = - (torch.stack([dist.log_prob(action) for dist, action in zip([torch.distributions.Categorical(actor(state)) for state in states], actions)]) * advantages).mean()
        critic_loss = advantages.pow(2).mean()
        actor_optimizer.zero_grad()
        actor_loss.backward(retain_graph=True)
        actor_optimizer.step()
        critic_optimizer.zero_grad()
        critic_loss.backward()
        critic_optimizer.step()
    return rewards_all_episodes

3. Rainbow - DQN

Rainbow - DQN融合了多种DQN改进技术，像Double DQN、Dueling DQN、Prioritized Experience Replay等，提升了算法的稳定性和效率。

4. PPO - discrete

近端策略优化（PPO）离散版本，它通过优化一个裁剪的目标函数来更新策略，防止策略更新过大。

5. PPO - continous

PPO连续版本，适用于连续动作空间的环境。

6. DDPG

深度确定性策略梯度（DDPG），结合了DQN和确定性策略梯度，用于连续动作空间。

7. TD3

TD3是对DDPG的改进，通过延迟策略更新、双Q网络等方法，提升算法稳定性。

8. SAC

软演员 - 评论家（SAC），基于最大熵原理，在学习最优策略的同时最大化策略的熵，增加探索性。

9. PPO - discrete - RNN

带有循环神经网络（RNN）的离散PPO版本，适用于处理序列相关的状态信息。

三、总结

这9种DRL算法涵盖了基于策略梯度、基于价值以及二者结合的多种方法，对于强化学习初学者来说，是非常好的学习资源。通过实践这些算法，你能深入理解强化学习的核心概念，掌握使用Pytorch进行强化学习算法开发的技能。赶紧动手试试吧！