多代理连续控制与产生性流量网络.
Shuang Luo1, Yinchuan Li2, Shunyu Liu3
1School of Public Affairs, Zhejiang University, Hangzhou 310027, China.
概括
本研究介绍了多代理生成连续流网络 (MACFN),用于在多代理系统中进行合作勘探. 通过将全球奖励分解为当地代理人的贡献,提高勘探能力,MACFN实现了分散的控制.
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:
- 生成性流网络 (GFlowNets) 为探索性控制任务提供了强化学习的替代方案.
- GFlowNets的个人流量匹配约束阻碍了多代理系统应用,特别是在连续控制中.
研究的目的:
- 提出多代理生成连续流网络 (MACFN) 在多代理系统中进行合作勘探.
- 在连续的联合控制问题中,使多个代理商的分散控制成为可能.
主要方法:
- MACFN采用集中式培训方法,执行方式分散.
- 一个连续流分解网络将单个代理流的贡献从总体奖励中扣除.
- 政策以基于全球流的匹配方式进行训练,使得基于本地流的分散行动选择成为可能.
主要成果:
- 与最先进的方法相比,MACFN表现出卓越的性能.
- 拟议的方法增强了在多代理连续控制任务中的勘探能力.
- 一个一致性条件的理论推导确保了连续流分解的表达性.
结论:
- 在多代理连续控制方面,MACFN有效地解决了GFlowNets的局限性.
- 该方法促进了对组成连续物体的合作勘探和分散控制.
- MACFN为先进的多代理增强学习和探索提供了一个有前途的框架.
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