MACRPO:多个代理合作经常性政策优化优化
1Intelligent Robotics Group, Electrical Engineering and Automation Department, Aalto University, Helsinki, Finland.
Frontiers in robotics and AI
|January 6, 2025
概括
本研究介绍了多代理合作反复近距离政策优化 (MACRPO) 以改善复杂,不沟通的环境中的代理合作. MACRPO增强了信息共享和处理部分可观测性,优于现有的多代理算法.
科学领域:
- 人工智能的人工智能
- 强化学习是一种强化学习.
- 多代理系统 多代理系统
背景情况:
- 多代理系统在没有通信的部分可观测和非静止环境中面临挑战.
- 有效的信息共享和协调对于合作政策学习至关重要.
研究的目的:
- 为增强合作政策学习提出一种新的多代理主体-关键方法,MACRPO.
- 在具有挑战性的多代理环境中,改进跨代理和时间信息集成.
主要方法:
- 在批评者的网络架构中实现了一个循环层,并使用了一个元轨迹训练框架.
- 开发了一种新的优势功能,结合其他代理人的奖励和价值功能,由合作参数控制.
- 在Deepdrive-Zero,多步行者和粒子环境上评估了MACRPO.
主要成果:
- 与最先进的多代理算法和单代理方法相比,MACRPO表现出更高的性能.
- 循环层有效地学习了合作,代理相互作用,并处理了部分可观测性.
- 拟议的优势功能允许控制合作水平.
结论:
- 在具有挑战性的多代理情景中,MACRPO为学习合作政策提供了一个强大的解决方案.
- 该方法的新型组件有效地解决了部分可观测性,并促进了代理之间的协调.
- 这些发现表明,MACRPO有潜力推进合作型多代理强化学习的研究.
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