请以Python源代码的形式给出答案，在一个小车推箱子游戏中，如何使用深度强化学习来控制小车的移动，要求输出必须是代码形式的完整实现，须包含示例模型的训练和推断。
user
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from collections import deque
import random
import numpy as np

# 定义神经网络模型
class QNetwork(nn.Module):
    def __init__(self, state_size, action_size):
        super(QNetwork, self).__init__()
        self.fc1 = nn.Linear(state_size, 128)
        self.fc2 = nn.Linear(128, 128)
        self.fc3 = nn.Linear(128, action_size)

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

# 定义经验回放缓冲区
class ReplayBuffer:
    def __init__(self, capacity):
        self.buffer = deque(maxlen=capacity)

    def push(self, state, action, reward, next_state, done):
        self.buffer.append((state, action, reward, next_state, done))

    def sample(self, batch_size):
        return random.sample(self.buffer, batch_size)

    def __len__(self):
        return len(self.buffer)

# 定义DQN代理
class DQNAgent:
    def __init__(self, state_size, action_size, epsilon=1.0, epsilon_min=0.01, epsilon_decay=0.995, gamma=0.99, lr=0.001, buffer_size=10000):
        self.state_size = state_size
        self.action_size = action_size
        self.epsilon = epsilon
        self.epsilon_min = epsilon_min
        self.epsilon_decay = epsilon_decay
        self.gamma = gamma
        self.lr = lr
        self.buffer = ReplayBuffer(buffer_size)
        self.q_network = QNetwork(state_size, action_size)
        self.target_network = QNetwork(state_size, action_size)
        self.target_network.load_state_dict(self.q_network.state_dict())
        self.optimizer = optim.Adam(self.q_network.parameters(), lr=self.lr)

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        state = torch.tensor(state, dtype=torch.float32).unsqueeze(0)
        q_values = self.q_network(state)
        action = torch.argmax(q_values, dim=1).item()
        return action

    def train(self, batch_size):
        if len(self.buffer) < batch_size:
            return
        minibatch = self.buffer.sample(batch_size)
        states, actions, rewards, next_states, dones = zip(*minibatch)

        states = torch.tensor(states, dtype=torch.float32)
        actions = torch.tensor(actions, dtype=torch.long).unsqueeze(1)
        rewards = torch.tensor(rewards, dtype=torch.float32).unsqueeze(1)
        next_states = torch.tensor(next_states, dtype=torch.float32)
        dones = torch.tensor(dones, dtype=torch.float32).unsqueeze(1)

        q_values = self.q_network(states).gather(1, actions)
        next_q_values = self.target_network(next_states).max(1)[0].unsqueeze(1)
        target_q_values = rewards + self.gamma * next_q_values * (1 - dones)

        loss = F.mse_loss(q_values, target_q_values)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

    def update_target_network(self):
        self.target_
assistant