强化学习中的迁移学习与终身学习

    import gym
    import torch
    import torch.nn as nn
    import torch.optim as optim
    from torchvision import models
    
    # 定义源域和目标域的环境
    source_env = gym.make('CartPole-v0')
    target_env = gym.make('CartPole-v1')
    
    # 构建特征提取网络和对抗性网络
    feature_extractor = models.resnet18(pretrained=True)
    feature_extractor.fc = nn.Linear(feature_extractor.fc.in_features, 64)
    domain_classifier = nn.Sequential(
    nn.Linear(64, 32),
    nn.ReLU(),
    nn.Linear(32, 2)
    )
    
    # 训练过程
    for epoch in range(num_epochs):
    # 源域样本训练特征提取网络
    source_obs = source_env.reset()
    source_features = feature_extractor(source_obs)
    source_labels = torch.zeros(batch_size, dtype=torch.long)
    source_loss = criterion(domain_classifier(source_features), source_labels)
    source_loss.backward()
    optimizer.step()
    
    # 目标域样本训练对抗性网络
    target_obs = target_env.reset()
    target_features = feature_extractor(target_obs)
    target_labels = torch.ones(batch_size, dtype=torch.long)
    target_loss = criterion(domain_classifier(target_features), target_labels)
    target_loss.backward()
    optimizer.step()
    
    # 联合优化特征提取网络和对抗性网络
    domain_loss = source_loss + target_loss
    domain_loss.backward()
    optimizer.step()
    
    # 在目标域上fine-tune特征提取网络
    feature_extractor.fc = nn.Linear(feature_extractor.fc.in_features, target_env.action_space.n)
    feature_extractor.train()
    for epoch in range(num_epochs):
    obs = target_env.reset()
    action = feature_extractor(obs).argmax().item()
    next_obs, reward, done, _ = target_env.step(action)
    loss = criterion(feature_extractor(next_obs), action)
    loss.backward()
    optimizer.step()

    import gym
    import torch
    import torch.nn as nn
    import torch.optim as optim
    from maml import MAML
    
    # 定义一系列CartPole变体环境
    envs = [gym.make('CartPole-v0'), gym.make('CartPole-v1'), gym.make('CartPole-v2')]
    
    # 构建MAML模型
    model = MAML(state_dim, action_dim, hidden_size)
    optimizer = optim.Adam(model.parameters(), lr=meta_lr)
    
    # 训练过程
    for epoch in range(num_epochs):
    meta_train_loss = 0
    for task in envs:
        # 对每个任务进行一步或多步梯度更新
        task_params = model.forward(task.reset())
        for _ in range(num_steps):
            action = task_params.argmax().item()
            next_obs, reward, done, _ = task.step(action)
            loss = criterion(task_params, action)
            loss.backward()
            task_params.grad.data.clamp_(-10, 10)
            task_params = task_params - lr * task_params.grad
    
        # 计算任务损失,并对模型参数进行梯度更新
        meta_train_loss += loss.item()
    meta_train_loss /= len(envs)
    meta_train_loss.backward()
    optimizer.step()
    
    # 在新任务上进行快速适应
    new_env = gym.make('CartPole-v3')
    adapted_params = model.forward(new_env.reset())
    for _ in range(num_steps):
    action = adapted_params.argmax().item()
    next_obs, reward, done, _ = new_env.step(action)
    loss = criterion(adapted_params, action)
    loss.backward()
    adapted_params.grad.data.clamp_(-10, 10)
    adapted_params = adapted_params - lr * adapted_params.grad