代表性驱动的抽样和适应性政策重置,以改善多个代理人的强化学习
Weiqiang Jin1, Xingwu Tian2, Ningwei Wang1
1School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an, Shanxi, 710049, Shanxi, China.
概括
我们介绍了 eXJTU-MARL,这是多代理强化学习 (MARL) 的一种新方法,可以提高探索和学习效率. 这种方法克服了常见的MARL挑战,如政策融合和复杂环境中的采样效率低下.
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) 对于复杂的决策至关重要,但面临着政策融合和抽样效率低下等挑战.
- 大规模的国家,观察和行动空间加剧了这些问题,导致了低于最佳的战略和广泛的培训需求.
研究的目的:
- 提出一种新的 MARL 方法,eXJTU-MARL,提高探索和轨迹学习效率.
- 为了解决主流MARL算法中不足的勘探和采样效率低下的问题.
主要方法:
- 引入适应性政策重置以防止过早的趋同.
- 发展基于国家代表性的平衡经验抽样,以提高数据效率.
- 在eXJTU-MARL框架内执行多个代理人的决策任务.
主要成果:
- eXJTU-MARL显著提高了采样效率和勘探能力.
- 这种方法可以防止过早的趋同到低于最佳的政策.
- 在星际飞船多代理挑战中的实验显示,在现有的MARL基线上表现优越.
结论:
- eXJTU-MARL有效地提高了复杂的MARL环境中的探索和学习效率.
- 适应性政策重置和平衡的经验采样是关键贡献.
- 拟议的方法为先进的多代理决策提供了一个强大的解决方案.
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