深度强化学习代码gpt

以下是使用深度强化学习实现GPT的代码示例：

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

# 定义GPT模型
class GPT(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers):
        super(GPT, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_dim, vocab_size)
        
    def forward(self, x, h=None):
        x = self.embedding(x)
        out, h = self.lstm(x, h)
        out = self.fc(out)
        return out, h

# 定义强化学习训练函数
def train(model, data, optimizer, criterion, num_epochs=10):
    for epoch in range(num_epochs):
        total_loss = 0
        h = None
        for batch in data:
            x, y = batch
            optimizer.zero_grad()
            out, h = model(x, h)
            loss = criterion(out[:, :-1, :].reshape(-1, out.shape[2]), y[:, 1:].reshape(-1))
            loss.backward()
            optimizer.step()
            total_loss += loss.item()
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, total_loss/len(data)))

# 设置超参数
vocab_size = 10000
embedding_dim = 32
hidden_dim = 64
num_layers = 2
num_epochs = 10
batch_size = 32
lr = 0.01

# 生成训练数据
data = []
for i in range(1000):
    seq_len = np.random.randint(5, 20)
    seq = np.random.randint(0, vocab_size, seq_len)
    x = seq[:-1].reshape(1, -1)
    y = seq[1:].reshape(1, -1)
    data.append((torch.LongTensor(x), torch.LongTensor(y)))

# 初始化模型、优化器、损失函数
model = GPT(vocab_size, embedding_dim, hidden_dim, num_layers)
optimizer = optim.Adam(model.parameters(), lr=lr)
criterion = nn.CrossEntropyLoss()

# 训练模型
train(model, data, optimizer, criterion, num_epochs=num_epochs)

上述代码中，我们定义了一个GPT模型，使用LSTM作为基础模型，然后使用强化学习方法进行训练。在训练函数中，我们循环遍历数据集中的每一个batch，然后使用模型对输入进行预测，并计算预测结果与真实标签之间的交叉熵损失。最后使用反向传播更新模型参数。