合作式多代理学习和探索,具有最小至最大的内在动机
IEEE transactions on cybernetics
|April 18, 2025
概括
这项研究介绍了E2M,一种新的多代理强化学习 (MARL) 探索方法. 通过最大限度地减少惊喜和最大限度地提高社会影响力,以改善共同政策学习,E2M增强了代理合作.
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:
- 多代理强化学习 (MARL) 在协调探索中面临挑战,原因是状态不确定性和观察不一致性.
- 有效的探索对于在复杂,动态的环境中学习有益的政策至关重要.
研究的目的:
- 提出一种新的MARL勘探方法,E2M,以解决协调勘探的挑战.
- 加强多个代理商之间共同政策的学习.
主要方法:
- 引入了Minim-Max内在动机 (E2M),包括惊喜最小化和社会影响最大化.
- 采用状态来使用低维状态表示的惊喜估计.
- 利用代理行为之间的相互信息来最大限度地发挥社会影响.
主要成果:
- E2M在增强StarCraft II和多代理MuJoCo任务中的合作能力方面表现出有效性.
- 该方法成功地鼓励代理人应对不稳定的环境并以合作方式互动.
- 结果显示,通过减少意外和社会影响,共同政策学习得到了改善.
结论:
- E2M为MARL的协调勘探提供了一个强大的解决方案.
- 拟议的方法有效地平衡了在多代理系统中的勘探和合作.
- 对于推进MARL研究和应用,E2M显示出显著的前景.
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