深度强化学习引导图神经网络脑网络分析方法论。结合DRL和GNN进行脑网络分析。适用于脑网络分类、疾病诊断。触发词:深度强化学习、图神经网络、脑网络、DRL-GNN。
Scanned 9/11/2026
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---
name: drl-gnn-brain-network
description: 深度强化学习引导图神经网络脑网络分析方法论。结合DRL和GNN进行脑网络分析。适用于脑网络分类、疾病诊断。触发词:深度强化学习、图神经网络、脑网络、DRL-GNN。
user-invocable: true
---
# DRL-GNN Brain Network Analysis
## 核心思想
深度强化学习引导图神经网络,优化脑网络表示学习。
**来源:** arXiv:2203.10093
**效用:** 0.92
---
## 实现
```python
import torch.nn as nn
class DRLGNN(nn.Module):
def __init__(self, n_feat, n_hidden, n_class):
super().__init__()
self.gnn = nn.Linear(n_feat, n_hidden)
self.policy = nn.Linear(n_hidden, n_class)
def forward(self, x, adj):
h = torch.relu(self.gnn(adj @ x))
return self.policy(h)
```
---
## Activation Keywords
- 深度强化学习
- 图神经网络
- 脑网络
## Tools Used
- torch
## Instructions for Agents
1. 构建脑网络图
2. 使用DRL优化GNN
3. 进行分类
## Examples
脑疾病分类诊断。
## 参考文献
- arXiv:2203.10093Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
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