强大的多代理通信与图形信息瓶优化
IEEE transactions on pattern analysis and machine intelligence
|November 29, 2023
概括
本研究介绍了使用图形神经网络 (GNN) 进行多代理强化学习 (MARL) 的强有力的沟通学习. 该方法通过优化信息流来增强代理协调,改善扰动下的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 多代理强化学习 (MARL) 从沟通学习中获益,以改善行动协调.
- 图形神经网络 (GNN) 为MARL通信提供了一个框架,将模型代理和通道作为图形节点和边缘.
- 现有的基于GNN的MARL通信易受对抗性攻击和噪音的影响,这一挑战在很大程度上没有得到解决.
研究的目的:
- 为 MARL 系统开发一个强大的沟通学习机制.
- 增强基于GNN的通信对干扰的弹性.
- 为了优化MARL通信的有效性和稳定性.
主要方法:
- 引入了一个强大的通信学习机制,采用图形信息瓶优化.
- 开发了两个信息理论规范器来学习最小足够的消息表示.
- 在消息表示和动作选择之间最大限度地增加了相互信息.
- 尽量减少代理特征和消息表示之间的相互信息.
- 将通信机制集成到一个使用价值分解方法的 MARL 框架中.
主要成果:
- 与基于GNN的最先进的MARL技术相比,提出的方法显示出更高的稳定性.
- 实验结果证实了开发的通信学习机制的提高效率.
- 这种方法有效地减轻了噪音和对抗性干扰对MARL通信的影响.
结论:
- 图形信息瓶优化为强大的MARL通信提供了有效的策略.
- 拟议的规范化器允许学习高效和弹性通信协议.
- 这项工作通过解决基于GNN的通信中的关键漏洞,推动了强大的MARL领域的发展.
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