Ad hoc teamwork is the challenging problem of designing an autonomous agent
which can adapt quickly to collaborate with teammates without prior
coordination mechanisms, including joint training. Prior work in this area has
focused on closed teams in which the number of agents is fixed. In this work,
we consider open teams by allowing agents with different fixed policies to
enter and leave the environment without prior notification. Our solution builds
on graph neural networks to learn agent models and joint-action value models
under varying team compositions. We contribute a novel action-value computation
that integrates the agent model and joint-action value model to produce
action-value estimates. We empirically demonstrate that our approach
successfully models the effects other agents have on the learner, leading to
policies that robustly adapt to dynamic team compositions and significantly
outperform several alternative methods.

本文提出建立一个自主代理，该代理可以在没有先前的协调机制的情况下快速适应以与队友合作的挑战性问题，包括联合培训，该解决方案基于图神经网络学习代理模型和不同团队组成下的联合行动价值模型，并提出了新的行动价值计算方法， 经实验证明，该方法成功地模拟了其他代理对学习器的影响，导致了可以稳健地适应动态团队组成并显着优于若干替代方法的策略。