ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork

Abstract

Learning to collaborate with previously unseen partners is a fundamental generalization challenge, known as Ad Hoc Teamwork (AHT). Existing methods often adopt a two-stage pipeline: first, a fixed population of teammates is generated, and second, an AHT agent is trained to collaborate with them. This separation limits coverage of behaviors and ignores whether the generated teammates are informative for the AHT agent to learn from. On the other hand, AHT agents are typically trained under the assumption that the training teammate set is uncontrollable, despite the fact that its composition strongly influences generalization. This paper presents a unified framework for AHT by reformulating the problem as an open-ended learning process between an AHT agent and an adversarial teammate generator. We introduce ROTATE, a regret-driven, open-ended training algorithm that alternates between improving the AHT agent and generating teammates that probe its collaboration deficiencies. Experiments across Overcooked and Level-Based Foraging tasks demonstrate that ROTATE substantially outperforms baselines on an unseen set of teammates, establishing a new standard for robust, generalizable teamwork.