用图形神经网络进行深度强化学习的挑战和机会:对算法和应用的全面审查
概括
本综述探讨了深度强化学习 (DRL) 和图形神经网络 (GNN) 之间的协同作用. 它们的融合通过提高概括性和减少图形结构环境中的复杂性来增强人工智能应用.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 深度强化学习 (DRL) 在模式识别,机器人和游戏方面表现出色.
- 图形神经网络 (GNN) 在对图形结构数据的监督学习中表现出卓越的表现.
- DRL和GNN的整合是一个快速增长的研究领域.
研究的目的:
- 提供混合DRL-GNN工程的全面审查.
- 将这些工作分为算法和特定应用的贡献.
- 分析DRL和GNNs合并的好处和挑战.
主要方法:
- 现有DRL-GNN研究的分类.
- 对算法和特定应用程序贡献的分析.
- 评估可概括性和计算复杂性的改进.
主要成果:
- DRL和GNN的合并有效地解决了工程和生命科学领域的复杂问题.
- 混合方法提高了概括性,降低了计算复杂性.
- 贡献有两个主要类别:算法和特定应用程序.
结论:
- DRL和GNN的整合为推进人工智能提供了巨大的潜力.
- 未来的研究应该专注于克服整合挑战.
- 这种融合对机器学习社区具有广泛的兴趣.
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