Ad hoc teamwork poses a challenging problem, requiring the design of an agent to collaborate with teammates without prior coordination or joint training. Open ad hoc teamwork further complicates this challenge by considering environments with a changing number of teammates, referred to as open teams. The state-of-the-art solution to this problem is graph-based policy learning (GPL), leveraging the generalizability of graph neural networks to handle an unrestricted number of agents and effectively address open teams. GPL's performance is superior to other methods, but its joint Q-value representation presents challenges for interpretation, hindering further development of this research line and applicability. In this paper, we establish a new theory to give an interpretation for the joint Q-value representation employed in GPL, from the perspective of cooperative game theory. Building on our theory, we propose a novel algorithm based on GPL framework, to complement the critical features that facilitate learning, but overlooked in GPL. Through experiments, we demonstrate the correctness of our theory by comparing the performance of the resulting algorithm with GPL in dynamic team compositions.

建立一个从合作博弈理论的视角解释图神经网络在图基策略学习中使用的联合 Q 值表示法，并基于该理论提出了一种新的算法来补充 GPL 中被忽视但有助于学习的关键特征。通过实验证明了该算法在动态团队构成中与 GPL 的性能比较的正确性。

合作博弈理论下的开放式即兴团队合作