单个Q值的分解和转移用于决策的多代理强化学习与通信
Xiaopeng Xu1, Yadong Zhao1, Dong Wang1
1Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, School of Control Science and Engineering, Dalian University of Technology, Dalian, 116024, China.
概括
两种新的框架通过分离沟通和决策来简化多代理强化学习. 这种方法提高了学习效率和性能,在复杂的任务,如无人机战斗.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 多代理系统 多代理系统
背景情况:
- 部分可观测的多代理任务需要协调的沟通和决策.
- 通信和决策模块之间的合使多代理强化学习 (MARL) 变得复杂.
研究的目的:
- 提出新的基于转移的框架,以缓解MARL的合问题.
- 为了提高学习效率,使通信模块的独立培训成为可能.
主要方法:
- 引入了简单转移框架 (STF) 以将困难的RL问题转化为更容易的和监督学习.
- 开发了分解和转移框架 (DTF),以进一步将监督学习分解为本地Q值和偏差.
- 利用相互信息的神经估计来独立训练DTF中的消息生成器.
主要成果:
- 拟议的框架使用两个基于通信的架构进行了验证.
- 与直接强化学习算法相比,这两种框架都表现出优异的性能.
- 在无人机战斗环境中,在不同规模的团队中观察到性能改善.
- 独立培训有效地促进了学习偏差的转移.
结论:
- 拟议的基于转移的框架成功地解决了MARL中的合挑战.
- 通信模块的独立培训是增强学习的有效策略.
- DTF为未来的分散式MARL研究提供了一个有希望的方向.
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