Publication Type

Journal Article

Version

acceptedVersion

Publication Date

7-2025

Abstract

Open ad hoc teamwork presents the challenging problem of designing an autonomous agent that can rapidly adapt to collaborate with teammates without prior coordination in an open environment. Existing methods primarily rely on fixed, predefined teammate types, overlooking the fact that teammates may change dynamically. To address this limitation, we propose a novel reinforcement learning approach, the Open Online Teammate Adaptation Framework (Open-OTAF), which enables a controlled agent to collaborate with dynamic teammates in open ad hoc environments. To achieve this, the controlled agent employs a dual teamwork situation inference model to capture the current teamwork state, facilitating decision-making under partial observability. To handle the dynamic nature of teammate types, we first introduce a Chinese Restaurant Process-based model to categorize diverse teammate policies into distinct clusters, improving the efficiency of identifying teamwork situations. Next, to model heterogeneous agent relationships and accommodate a variable number of teammates, we represent the team as a heterogeneous graph and leverage heterogeneous graph attention neural networks to learn the representation of the teamwork situation. Extensive experiments across four challenging multi-agent benchmark tasks—Level-Based Foraging, Wolf-Pack, Cooperative Navigation, and FortAttack—demonstrate that our method successfully enables dynamic teamwork in open ad hoc settings. Open-OTAF outperforms state-of-the-art methods, achieving superior performance with faster convergence.

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Transactions on Machine Learning Research

Additional URL

https://openreview.net/forum?id=BukMU42P3G

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