社会满足:多个代理强化学习与满足代理
Daisuke Uragami1, Noriaki Sonota2, Tatsuji Takahashi2
1College of Industrial Technology, Nihon University, 1-2-1, Izumi, Narashino, Chiba, 275-8575, Japan.
Bio Systems
|July 20, 2024
概括
社会满足使多代理强化学习代理人能够通过分享愿望水平来有效地找到最佳解决方案. 这种新的框架提高了学习效率,并自主调整了探索范围.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 强化学习是一种强化学习.
背景情况:
- 强化学习代理需要有限的探索,以获得高效的试错学习.
- 限制探索可能会导致由于局部,自下而上的学习而导致非最佳解决方案.
- 风险敏感的满足 (RS) 价值函数提供了自上而下的方法,但需要适当的愿望水平.
研究的目的:
- 为了解决确定强化学习的愿望水平的开放问题.
- 提出社会满意,一个多代理强化学习的新框架.
- 提高代理人的学习效率和最佳性.
主要方法:
- 开发了一个社会满意的框架,用于多代理强化学习.
- 代理商通过信息共享来确定愿望水平.
- 将情节性愿望水平转换为地方,州级的愿望水平.
- 在一个具有挑战性的环境 (SuboptimaWorld) 中进行模拟,并设置了许多次优目标.
主要成果:
- 与现有方法相比,提出的社会满足方法显示了更高的学习效率.
- 该框架有效地阻止了代理商趋同到次优解决方案.
- 该方法显示了自主调整勘探范围的能力.
- 有效的学习需要最小的共享信息.
结论:
- 社会满足为多代理强化学习提供了一种有效的方法.
- 该框架提高了学习效率和解决方案的最佳性.
- 这项研究提供了对人工智能和机器学习相关的社会行为的一些见解.
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