PPO,RLHF,微调技术：综合应用与未来展望

    import torch
    import torch.nn as nn
    import torch.optim as optim
    from torch.distributions import Categorical
    
    class PPOAgent(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim=64):
        super(PPOAgent, self).__init__()
        self.fc1 = nn.Linear(state_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, action_dim)
        self.optimizer = optim.Adam(self.parameters(), lr=3e-4)
        self.old_log_probs = None
        self.rewards = []
        self.states = []
        self.actions = []
    
    def forward(self, x):
        x = torch.tanh(self.fc1(x))
        return self.fc2(x)
    
    def act(self, state):
        state = torch.from_numpy(state).float().unsqueeze(0)
        probs = torch.softmax(self(state), dim=1)
        dist = Categorical(probs)
        action = dist.sample()
        log_prob = dist.log_prob(action)
        self.states.append(state)
        self.actions.append(action.item())
        self.old_log_probs = log_prob
        return action.item()
    
    def update(self, gamma=0.99, epsilon=0.2):
        R = 0
        policy_losses = []
        for reward in self.rewards[::-1]:
            R = reward + gamma * R
            policy_losses.insert(0, -self.old_log_probs * R)
        policy_loss = torch.cat(policy_losses).mean()
    
        new_probs = torch.softmax(self(torch.cat(self.states)), dim=1)
        new_dist = Categorical(new_probs)
        new_log_probs = new_dist.log_prob(torch.tensor(self.actions))
        ratio = torch.exp(new_log_probs - self.old_log_probs.detach())
        clip_ratio = torch.clamp(ratio, 1 - epsilon, 1 + epsilon)
        policy_loss = -torch.min(ratio * R, clip_ratio * R).mean()
    
        self.optimizer.zero_grad()
        policy_loss.backward()
        self.optimizer.step()
    
        self.rewards = []
        self.states = []
        self.actions = []
        self.old_log_probs = None

    import torch
    import torch.nn as nn
    import torch.optim as optim
    from torch.distributions import Categorical
    
    class RLHFAgent(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim=64):
        super(RLHFAgent, self).__init__()
        self.policy = PPOAgent(state_dim, action_dim, hidden_dim)
        self.reward_model = nn.Sequential(
            nn.Linear(state_dim + action_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 1)
        )
        self.reward_optimizer = optim.Adam(self.reward_model.parameters(), lr=1e-3)
    
    def act(self, state):
        return self.policy.act(state)
    
    def update_policy(self, gamma=0.99, epsilon=0.2):
        self.policy.update(gamma, epsilon)
    
    def update_reward_model(self, states, actions, human_rewards):
        states = torch.cat(states)
        actions = torch.tensor(actions)
        human_rewards = torch.tensor(human_rewards).unsqueeze(1)
        model_rewards = self.reward_model(torch.cat([states, actions], dim=1))
        loss = nn.MSELoss()(model_rewards, human_rewards)
        self.reward_optimizer.zero_grad()
        loss.backward()
        self.reward_optimizer.step()
    
    def train(self, max_episodes=1000):
        for episode in range(max_episodes):
            states, actions, human_rewards = self.collect_samples()
            self.update_reward_model(states, actions, human_rewards)
            self.update_policy()
    
    def collect_samples(self):
        states, actions, human_rewards = [], [], []
        done = False
        state = env.reset()
        while not done:
            action = self.act(state)
            next_state, reward, done, _ = env.step(action)
            human_reward = self.reward_model(torch.cat([torch.from_numpy(state).float(), torch.tensor([action])], dim=0)).item()
            states.append(torch.from_numpy(state).float())
            actions.append(action)
            human_rewards.append(human_reward)
            state = next_state
        return states, actions, human_rewards

    import torch
    import torch.nn as nn
    import torch.optim as optim
    
    class FineTuneModel(nn.Module):
    def __init__(self, pretrained_model, num_classes):
        super(FineTuneModel, self).__init__()
        ```python
        self.pretrained_model = pretrained_model
        self.classifier = nn.Linear(pretrained_model.fc.in_features, num_classes)
    
    def forward(self, x):
        x = self.pretrained_model(x)
        x = x.view(x.size(0), -1)  # Flatten the output from pretrained_model
        x = self.classifier(x)
        return x
    
    # Load a pre-trained model
    pretrained_model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)
    
    # Replace the last layer with a new layer for our specific number of classes
    num_classes = 10
    model = FineTuneModel(pretrained_model, num_classes)
    
    # Define a loss function and optimizer
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.classifier.parameters(), lr=0.001, momentum=0.9)
    
    # Assume we have some data in train_data and train_labels
    # Training loop
    for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(train_data):
        # Zero the parameter gradients
        optimizer.zero_grad()
    
        # Forward pass
        outputs = model(inputs)
        loss = criterion(outputs, labels)
    
        # Backward pass and optimization
        loss.backward()
        optimizer.step()
    
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, len(train_data), loss.item()))
    
    # Save the fine-tuned model
    torch.save(model.state_dict(), 'finetuned_model.pt')