用pytorch的DQN写一个坐标转换模型

好的，下面是一个用PyTorch实现的基于DQN算法的坐标转换模型示例：

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

class DQN(nn.Module):
    def __init__(self, input_size, output_size):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(input_size, 64)
        self.fc2 = nn.Linear(64, 128)
        self.fc3 = nn.Linear(128, 64)
        self.fc4 = nn.Linear(64, output_size)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = torch.relu(self.fc3(x))
        x = self.fc4(x)
        return x

class DQNAgent:
    def __init__(self, input_size, output_size, gamma=0.99, epsilon=1.0, epsilon_min=0.01, epsilon_decay=0.995, lr=0.001, memory_size=100000):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.input_size = input_size
        self.output_size = output_size
        self.gamma = gamma
        self.epsilon = epsilon
        self.epsilon_min = epsilon_min
        self.epsilon_decay = epsilon_decay
        self.lr = lr
        self.memory = []
        self.memory_size = memory_size
        self.model = DQN(input_size, output_size).to(self.device)
        self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
        self.loss_fn = nn.MSELoss()

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.randint(self.output_size)
        state = torch.tensor(state, dtype=torch.float32).to(self.device)
        q_values = self.model(state)
        return torch.argmax(q_values).item()

    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]

    def replay(self, batch_size):
        if len(self.memory) < batch_size:
            return
        batch = np.random.choice(len(self.memory), batch_size, replace=False)
        for i in batch:
            state, action, reward, next_state, done = self.memory[i]
            state = torch.tensor(state, dtype=torch.float32).to(self.device)
            next_state = torch.tensor(next_state, dtype=torch.float32).to(self.device)
            action = torch.tensor([action], dtype=torch.int64).to(self.device)
            reward = torch.tensor([reward], dtype=torch.float32).to(self.device)
            done = torch.tensor([done], dtype=torch.float32).to(self.device)
            q_values = self.model(state)
            next_q_values = self.model(next_state)
            target = reward + (1 - done) * self.gamma * torch.max(next_q_values)
            target = target.detach()
            loss = self.loss_fn(q_values.gather(1, action.unsqueeze(1)), target.unsqueeze(1))
            self.optimizer.zero_grad()
            loss.backward()
            self.optimizer.step()
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

这个模型的输入是一个由两个浮点数构成的向量，表示坐标的x和y值，输出是一个整数，表示坐标所在的象限。具体实现中，我们使用了一个四层的全连接神经网络作为DQN模型，使用Adam优化器和MSE损失函数进行训练。在训练过程中，我们使用经验回放和ε-greedy策略来提高模型的效率和稳定性。

使用该模型进行坐标转换的示例代码如下：

agent = DQNAgent(input_size=2, output_size=4)
for episode in range(1000):
    state = [np.random.uniform(-10, 10), np.random.uniform(-10, 10)]
    for step in range(100):
        action = agent.act(state)
        if action == 0:
            next_state = [state[0], state[1]]
        elif action == 1:
            next_state = [state[0], -state[1]]
        elif action == 2:
            next_state = [-state[0], state[1]]
        else:
            next_state = [-state[0], -state[1]]
        reward = 1 if next_state[0] * next_state[1] > 0 else -1
        agent.remember(state, action, reward, next_state, False)
        state = next_state
        agent.replay(32)

在这个示例中，我们训练了一个坐标转换模型，将随机生成的坐标逐步转换为所在象限的编号。具体地，我们通过随机生成一个坐标作为初始状态，然后根据模型的输出选择一个象限进行转换，并计算奖励。最后，我们使用经验回放和ε-greedy策略来训练模型，使其能够准确地将坐标转换为象限编号。