一种半独立的政策培训方法,为异质的多个代理人提供共享代表,以加强学习
Biao Zhao1, Weiqiang Jin1, Zhang Chen2
1School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an, China.
Frontiers in neuroscience
|July 5, 2023
概括
这项研究引入了一项新的半独立培训政策,用于合作多重强化学习中的异质代理人. 该方法通过使代理人能够共享抽象知识来增强学习,优于现有的算法.
科学领域:
- 人工智能的人工智能
- 神经科学是一个神经科学.
- 机器学习 机器学习
背景情况:
- 合作的多强化学习 (RL) 通过参数共享与同质代理优异.
- 不同质的代理对参数共享带来了挑战,因为不同的输入,输出和目标.
- 神经科学揭示了分享类似经验和抽象概念的大脑机制.
研究的目的:
- 提出一个半独立的培训政策,以合作多重强化学习的异质代理人.
- 解决参数共享和在不同代理环境中的专业培训之间的冲突.
- 为了利用大脑启发的知识共享机制来提高RL的表现.
主要方法:
- 一个半独立的培训政策,整合了观察和行动的共同代表.
- 利用共享的潜伏空间来平衡上游政策和下游代理职能.
- 在人类大脑中实施一种以分层知识共享为灵感的方法.
主要成果:
- 拟议的方法表现出优越的性能,与 heterogeneous 代理的主流算法相比.
- 经验证据支持该方法对课程学习和表示转移的概括性.
- 该方法有效地解决了各种代理投入,输出和目标的挑战.
结论:
- 新的半独立培训政策为异质的多代理强化学习提供了强有力的解决方案.
- 灵感来自大脑的方法有助于各种代理人之间有效的知识和经验分享.
- 该框架为高级异质代理RL提供了基础,包括课程学习和代表转移.
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