MuDE:基于奖励的多代理分解勘探
Byunghyun Yoo1, Sungwon Yi1, Hyunwoo Kim1
1Electronics and Telecommunications Research Institute (ETRI), 218 Gajeong-ro, Yuseong-gu, Daejeon, 34129, South Korea.
概括
本研究介绍了基于奖励的多代理分解探索 (MuDE),用于合作强化学习. 通过专注于积极的副奖励,改善合作行为和超越现有方法,MuDE增强了探索.
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:
- 合作的多代理强化学习 (MARL) 使用共享奖励和价值函数分解.
- 从奖励中估计个体代理人的贡献至关重要,但具有挑战性.
- 相互矛盾的强化和惩罚信号使学习合作行为变得复杂.
研究的目的:
- 提出一种新的勘探方案,即基于奖励的多代理分解勘探 (MuDE).
- 为了应对奖励分解和MARL中相互矛盾的信号的挑战.
- 改进行动空间的探索和合作行为学习.
主要方法:
- 开发了MuDE,这是一个修改后的奖励分解方案,用于积极的次奖励的优先探索.
- 将 MuDE 集成到 MARL 框架中.
- 在StarCraft II中评估了MuDE的微观管理和掠食者-猎物任务,并进行了强化和惩罚.
主要成果:
- MuDE准确地估计了副奖励.
- MuDE有效地探索了以前无法到达的行动空间.
- 与最先进的方法相比,MuDE在融合速度和获胜率方面表现优越.
结论:
- MuDE提供了一个有效的解决方案,用于奖励分解和探索在MARL.
- 拟议的方法增强了复杂环境中的合作行为学习.
- MuDE代表了多代理强化学习研究的重大进展.
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