N-Agent Ad Hoc Teamwork

Current approaches to learning cooperative behaviors in multi-agent settings assume relatively restrictive settings. In standard fully cooperative multi-agent reinforcement learning, the learning algorithm controls \textit{all} agents in the scenario, while in ad hoc teamwork, the learning algorithm usually assumes control over only a $\textit{single}$ agent in the scenario. However, many cooperative settings in the real world are much less restrictive. For example, in an autonomous driving scenario, a company might train its cars with the same learning algorithm, yet once on the road, these cars must cooperate with cars from another company. Towards generalizing the class of scenarios that cooperative learning methods can address, we introduce $N$-agent ad hoc teamwork, in which a set of autonomous agents must interact and cooperate with dynamically varying numbers and types of teammates at evaluation time. This paper formalizes the problem, and proposes the $\textit{Policy Optimization with Agent Modelling}$ (POAM) algorithm. POAM is a policy gradient, multi-agent reinforcement learning approach to the NAHT problem, that enables adaptation to diverse teammate behaviors by learning representations of teammate behaviors. Empirical evaluation on StarCraft II tasks shows that POAM improves cooperative task returns compared to baseline approaches, and enables out-of-distribution generalization to unseen teammates.