科普斯罗:一个离线的实证游戏理论方法与保守的批评家
IEEE transactions on neural networks and learning systems
|October 1, 2024
概括
这项研究引入了一种新方法,可以从历史数据中找到近似的纳什平衡 (NE),而无需新的相互作用. COPSRO算法提高了数据效率,适用于风险敏感的应用.
科学领域:
- 人工智能的人工智能
- 游戏理论 游戏理论
- 机器学习 机器学习
背景情况:
- 实证游戏理论分析 (EGTA) 通常需要广泛的环境相互作用来建模复杂的多代理系统.
- 现有方法的数据利用效率很低,对于风险敏感的应用程序来说通常是不可行的.
研究的目的:
- 开发一种新的算法,用于在线环境中从静态的历史数据集中学习近似的纳什平衡 (NE).
- 解决EGTA中主动数据收集的局限性,特别是对于不良风险情景.
主要方法:
- 引入了保守的离线政策空间响应Oracle (COPSRO) 算法,用于从固定的数据集中识别NE.
- 从线下数据构建了一个过度完整的策略群体,以接近游戏的政策空间.
- 集成了一个保守的批评者 (CC),以减轻线下学习中常见的高估问题.
- 开发了一个离线NE解决器,用于对近似NE的代计算.
主要成果:
- 科普斯罗成功地确定了平衡策略,而不需要现实世界的互动.
- 与现有方法相比,该算法显示出更高的收性和可利用性,特别是数据集覆盖率低 (10-20%).
- 经验评估验证了COPSRO在各种现实任务中的有效性.
结论:
- 科普斯罗为在线环境中学习NE提供了一种可行和高效的方法,提高了在不愿冒风险领域的适用性.
- 该方法在处理有限的历史数据时,显著改善了数据利用和性能,而不是传统的EGTA方法.
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