GCN和DQN融合后的算法代码

GCN和DQN的融合算法代码可以参考以下示例：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from torch_geometric.nn import GCNConv

class GCNDQN(nn.Module):
    def __init__(self, num_nodes, num_actions):
        super(GCNDQN, self).__init__()
        self.num_nodes = num_nodes
        self.num_actions = num_actions
        
        self.gc1 = GCNConv(num_features, 16)
        self.gc2 = GCNConv(16, 16)
        self.fc1 = nn.Linear(16 * num_nodes, 64)
        self.fc2 = nn.Linear(64, num_actions)
        
    def forward(self, x, edge_index):
        x = F.relu(self.gc1(x, edge_index))
        x = F.relu(self.gc2(x, edge_index))
        x = x.view(-1, 16 * self.num_nodes)
        x = F.relu(self.fc1(x))
        q_values = self.fc2(x)
        return q_values
    
class ReplayBuffer(object):
    def __init__(self, capacity):
        self.capacity = capacity
        self.memory = []
        self.position = 0
        
    def push(self, state, action, reward, next_state, done):
        transition = (state, action, reward, next_state, done)
        if len(self.memory) < self.capacity:
            self.memory.append(None)
        self.memory[self.position] = transition
        self.position = (self.position + 1) % self.capacity
        
    def sample(self, batch_size):
        return random.sample(self.memory, batch_size)
    
    def __len__(self):
        return len(self.memory)
    
class GCNDQNAgent(object):
    def __init__(self, num_nodes, num_actions, lr, gamma, epsilon, buffer_capacity, batch_size):
        self.num_nodes = num_nodes
        self.num_actions = num_actions
        self.gamma = gamma
        self.epsilon = epsilon
        self.batch_size = batch_size
        
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
        self.model = GCNDQN(num_nodes, num_actions).to(self.device)
        self.target_model = GCNDQN(num_nodes, num_actions).to(self.device)
        self.target_model.load_state_dict(self.model.state_dict())
        self.target_model.eval()
        
        self.optimizer = optim.Adam(self.model.parameters(), lr=lr)
        self.buffer = ReplayBuffer(buffer_capacity)
        
    def select_action(self, state):
        if np.random.rand() < self.epsilon:
            return np.random.randint(self.num_actions)
        with torch.no_grad():
            state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
            q_values = self.model(state)
            return q_values.argmax().item()
        
    def update(self):
        if len(self.buffer) < self.batch_size:
            return
        
        transitions = self.buffer.sample(self.batch_size)
        batch_state, batch_action, batch_reward, batch_next_state, batch_done = zip(*transitions)
        
        batch_state = torch.FloatTensor(batch_state).to(self.device)
        batch_action = torch.LongTensor(batch_action).to(self.device)
        batch_reward = torch.FloatTensor(batch_reward).to(self.device)
        batch_next_state = torch.FloatTensor(batch_next_state).to(self.device)
        batch_done = torch.FloatTensor(batch_done).to(self.device)
        
        q_values = self.model(batch_state).gather(1, batch_action.unsqueeze(1)).squeeze(1)
        next_q_values = self.target_model(batch_next_state).max(1)[0]
        expected_q_values = batch_reward + (1 - batch_done) * self.gamma * next_q_values
        
        loss = F.mse_loss(q_values, expected_q_values)
        
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
        
    def update_target_model(self):
        self.target_model.load_state_dict(self.model.state_dict())

这段代码实现了一个基于GCN和DQN的强化学习算法。其中，GCNDQN类定义了GCN和DQN的结合模型，ReplayBuffer类定义了经验回放缓存，GCNDQNAgent类定义了强化学习代理的训练和推断过程。

请根据具体的问题和需求进行适当修改和调整，以适应你的应用场景。希望对你有所帮助！