基于合作博弈抽象的团队合作评价与奖励
Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions
摘要：
我们能预测一个团队的个人在一起的表现吗？个人对团队绩效的贡献应该如何得到奖励？合作博弈论为我们提供了一套强大的工具来回答这些问题：特征函数（CF）和解决方案的概念，如沙普利值（SV）。将这些技术应用于现实世界的问题有两个主要困难：首先，CF很少给我们，需要从数据中学习。第二，SV本质上是组合的。我们引入了一个参数模型，称为合作博弈抽象（CGA）估计CF的数据。CGA易于学习，易于解释，关键是允许SV的线性时间计算。我们提供的CGA模型的识别结果和样本的复杂性界限，以及在估计的SV使用CGA的误差界。我们将我们的方法应用于研究人工RL代理团队以及来自职业体育的真实世界团队。
英文摘要：
Can we predict how well a team of individuals will perform together? How should individuals be rewarded for their contributions to the team performance? Cooperative game theory gives us a powerful set of tools for answering these questions: the Characteristic Function (CF) and solution concepts like the Shapley Value (SV). There are two major difficulties in applying these techniques to real world problems: first, the CF is rarely given to us and needs to be learned from data. Second, the SV is combinatorial in nature. We introduce a parametric model called cooperative game abstractions (CGAs) for estimating CFs from data. CGAs are easy to learn, readily interpretable, and crucially allow linear-time computation of the SV. We provide identification results and sample complexity bounds for CGA models as well as error bounds in the estimation of the SV using CGAs. We apply our methods to study teams of artificial RL agents as well as real world teams from professional sports.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
