A quantitative assessment of the global importance of an agent in a team is as valuable as gold for strategists, decision-makers, and sports coaches. Yet, retrieving this information is not trivial since in a cooperative task it is hard to isolate the performance of an individual from the one of the whole team. Moreover, it is not always clear the relationship between the role of an agent and his personal attributes. In this work we conceive an application of the Shapley analysis for studying the contribution of both agent policies and attributes, putting them on equal footing. Since the computational complexity is NP-hard and scales exponentially with the number of participants in a transferable utility coalitional game, we resort to exploiting a-priori knowledge about the rules of the game to constrain the relations between the participants over a graph. We hence propose a method to determine a Hierarchical Knowledge Graph of agents' policies and features in a Multi-Agent System. Assuming a simulator of the system is available, the graph structure allows to exploit dynamic programming to assess the importances in a much faster way. We test the proposed approach in a proof-of-case environment deploying both hardcoded policies and policies obtained via Deep Reinforcement Learning. The proposed paradigm is less computationally demanding than trivially computing the Shapley values and provides great insight not only into the importance of an agent in a team but also into the attributes needed to deploy the policy at its best.
翻译:对团队中代理人的全球重要性进行量化评估,对于策略学家、决策者和体育教练来说,其价值与黄金一样宝贵。然而,检索这一信息并非微不足道,因为在合作任务中,很难将个人的业绩与整个团队中的一个人区分开来;此外,对于代理人的作用与其个人属性之间的关系并不总是十分清楚。在这项工作中,我们设想了对研究代理政策和属性的贡献进行模拟性分析的应用,将其置于同等地位。由于计算的复杂性与可转移的公用事业联盟游戏的参与者人数成倍增长的NP-硬性和比例,我们利用游戏规则的首要知识来限制参与者之间的关系,因此,我们提出了一个方法来确定代理人的政策和特征的高度知识图。假设系统具有模拟性,图表结构允许利用动态的方案编制来以更快的方式评估其重要性。我们用证据测试关于游戏规则的知识,通过图表限制参与者之间的关系。因此,我们提出了一个方法,用以确定代理人政策和特征的高度知识图案图解图,不仅能够将最宝贵的政策运用到深度的模型中,而且不能通过深层次的模型来评估。我们测试拟议在深层次的模型中测试拟议采用的一种方法,而不能把最需要的深层次的深度的模型引入。