有效的多代理政策适应与贝叶斯政策重用和视图不变的对比意识
1College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China.
概括
与视图不变对比意识 (BPR-VCA) 的贝叶斯政策重用增强了在部分可观测环境中的多代理学习. 这种人工智能框架改善了政策适应和对手识别,以在竞争场景中获得更好的表现.
科学领域:
- 人工智能的人工智能
- 多代理系统 多代理系统
- 强化学习是一种强化学习.
背景情况:
- 贝叶斯政策重用 (BPR) 能够适应非静止对手,但仅限于单个代理,完全可观测的设置.
- 部分可观测性和多代理协调对现有的政策调整框架构成重大挑战.
研究的目的:
- 提出BPR-VCA,将BPR与多视图对比学习集成在一起,以便在部分可观测的多代理环境中高效地调整政策.
- 为了使代理人能够从当地观察中学习,并且尽管信息有限,但在全球动态上达成共识.
主要方法:
- BPR-VCA利用局部观测轨迹进行信念更新,解决部分可观测性问题.
- 一个视图不变的对比意识 (VCA) 模块通过整合全球和当地观点来促进对环境变化的共识.
- 分散的在线信念更新允许代理人选择合适的联合政策.
主要成果:
- 在四个竞争场景中,BPR-VCA表现优越.
- 与最先进的基线相比,该框架实现了更高的情节性和累积奖励.
- 观察到更快,更准确的对手识别,以及更高的胜利率.
结论:
- 在政策调整方面,BPR-VCA有效地解决了部分可观测性和多代理协调方面的挑战.
- 拟议的方法为需要在动态环境中适应性AI代理的现实应用提供了强大的解决方案.
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