LJIR:学习联合行动在合作的多代理强化学习中的内在奖励
Zihan Chen1, Biao Luo1, Tianmeng Hu1
1School of Automation, Central South University, Changsha 410083, China.
概括
多代理强化学习 (MARL) 代理需要共同探索以找到最佳政策. 本研究介绍了学习联合行动内在奖励 (LJIR),以加强探索并防止MARL中的局部最佳.
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:
- 有效的探索对于强化学习的高回报至关重要.
- 多代理系统需要共同探索,以发现最佳的联合政策.
- 基于策略的MARL算法因探索挑战和共享奖励而面临过度拟合和局部优化问题.
研究的目的:
- 引入一个新的框架,学习联合行动内在奖励 (LJIR),以提高多代理强化学习的联合探索和表现.
- 在MARL语言中解决政策过拟合和局部优化问题.
主要方法:
- 根据代理人的状态和共同行动,LJIR构建了一个在线内在奖励,以指导勘探.
- 结合变压器和随机网络蒸来识别新状态以获得内在奖励.
- 动态调整勘探开发平衡,并保持政策不变.
- 提供一种灵活的方法来结合内在和外部奖励.
主要成果:
- 通过奖励新的国家,LJIR有效地引导代理人采取最佳的联合行动.
- 在SMAC基准指标上表现出最先进的性能.
- 在复杂的 MARL 任务中成功应对挑战.
结论:
- LJIR增强了MARL代理商的联合勘探能力.
- 该框架提高了整体性能,并克服了局部最佳.
- 对于现有的 MARL 算法,LJIR 提供了一个多功能解决方案.
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