JaxAHT: A JAX-Based Library for Ad Hoc Teamwork

2026-09-12

AI Summary

针对临时团队合作研究中的高计算成本和缺乏标准化基准问题，本文通过引入基于JAX的开源库JaxAHT来加速研究流程，并提供多样化的评估队友套件。

Abstract

Ad Hoc Teamwork (AHT) addresses the challenge of designing agents capable of coordinating with novel partners without prior coordination. However, progress in the field is hindered by the prohibitive computational cost of the AHT research lifecycle, the lack of standardized benchmark implementations, and the absence of a diverse, validated evaluation teammate suite. In this work, we introduce JaxAHT, the first open-source, JAX-based library designed to accelerate and standardize the AHT research lifecycle. Leveraging JAX's hardware acceleration and massive parallelization capabilities, JaxAHT provides a unified framework for teammate generation, ego agent training, and evaluation against unseen teammates, achieving approximately 95x wall-clock speedup over PyTorch counterparts. Alongside the library, we contribute a diverse suite of evaluation teammates across the domains of Level-Based Foraging, Overcooked, and Hanabi. To illustrate the value of the framework, we use it to conduct a large-scale, compute-controlled benchmark study comparing teammate generation and AHT agent learning methods, finding that no algorithm consistently performs best, and that agent modeling primarily offers benefits in role-based scenarios with diverse teammates.

Problem

• Ad Hoc Teamwork
• computational cost
• standardized benchmark
• evaluation teammate suite

Innovation

• JAX
• Ad Hoc Teamwork
• Hardware Acceleration
• Massive Parallelization
• Unified Framework