深度强化学习代码gpt

以下是一个简单的GPT-2模型的深度强化学习代码示例（使用Python和PyTorch框架）：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义GPT-2模型
class GPT2(nn.Module):
    def __init__(self, vocab_size, d_model, nhead, num_layers):
        super(GPT2, self).__init__()
        self.d_model = d_model
        self.nhead = nhead
        self.num_layers = num_layers
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.transformer = nn.Transformer(d_model, nhead, num_layers)
        self.fc = nn.Linear(d_model, vocab_size)

    def forward(self, x):
        x = self.embedding(x)
        output = self.transformer(x, x)
        output = self.fc(output)
        return output

# 定义Deep Q-Network (DQN) agent
class DQNAgent:
    def __init__(self, state_size, action_size, memory_size=10000, batch_size=32, gamma=0.99, epsilon=1.0, epsilon_min=0.01, epsilon_decay=0.995):
        self.state_size = state_size
        self.action_size = action_size
        self.memory_size = memory_size
        self.batch_size = batch_size
        self.gamma = gamma
        self.epsilon = epsilon
        self.epsilon_min = epsilon_min
        self.epsilon_decay = epsilon_decay
        self.memory = []
        self.model = GPT2(vocab_size=state_size, d_model=512, nhead=8, num_layers=6)
        self.target_model = GPT2(vocab_size=state_size, d_model=512, nhead=8, num_layers=6)
        self.optimizer = optim.Adam(self.model.parameters(), lr=0.0001)

    # 保存经验
    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))
        if len(self.memory) > self.memory_size:
            del self.memory[0]

    # 从经验中采样并训练模型
    def replay(self):
        if len(self.memory) < self.batch_size:
            return
        batch = np.random.choice(self.memory, self.batch_size)
        states = []
        targets = []
        for state, action, reward, next_state, done in batch:
            target = reward
            if not done:
                target += self.gamma * torch.max(self.target_model(next_state), dim=1)[0]
            target_f = self.model(state)
            target_f[0][action] = target
            states.append(state)
            targets.append(target_f)
        states = torch.cat(states, dim=0)
        targets = torch.cat(targets, dim=0)
        self.optimizer.zero_grad()
        loss = nn.MSELoss()(self.model(states), targets)
        loss.backward()
        self.optimizer.step()

    # 选择行动
    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.action_size)
        return torch.argmax(self.model(state)).item()

    # 更新target网络
    def update_target_model(self):
        self.target_model.load_state_dict(self.model.state_dict())

    # 降低epsilon值
    def decay_epsilon(self):
        self.epsilon = max(self.epsilon_min, self.epsilon * self.epsilon_decay)

这个代码示例实现了一个GPT-2模型的深度强化学习代理，该代理使用了Deep Q-Network (DQN)算法来学习选择下一个词语。其中，`GPT2`类是一个简单的GPT-2模型，`DQNAgent`类是一个使用DQN算法的深度强化学习代理。该代理使用经验回放和目标网络来提高学习效率，并且在每个回合结束后降低epsilon值以逐渐减少探索行为。