The design of environments plays a critical role in shaping the development and evaluation of cooperative multi-agent reinforcement learning (MARL) algorithms. While existing benchmarks highlight critical challenges, they often lack the modularity required to design custom evaluation scenarios. We introduce the Totally Accelerated Battle Simulator in JAX (TABX), a high-throughput sandbox designed for reconfigurable multi-agent tasks. TABX provides granular control over environmental parameters, permitting a systematic investigation into emergent agent behaviors and algorithmic trade-offs across a diverse spectrum of task complexities. Leveraging JAX for hardware-accelerated execution on GPUs, TABX enables massive parallelization and significantly reduces computational overhead. By providing a fast, extensible, and easily customized framework, TABX facilitates the study of MARL agents in complex structured domains and serves as a scalable foundation for future research. Our code is available at: https://github.com/ku-dmlab/TABX.

翻译：环境设计在塑造合作型多智能体强化学习（MARL）算法的开发与评估中起着关键作用。现有基准虽凸显了关键挑战，但往往缺乏设计自定义评估场景所需的模块化能力。我们提出了基于JAX的全加速对战模拟器（TABX），这是一种面向可重构多智能体任务的高通量沙盒工具。TABX对环境参数提供精细控制，允许系统性地研究不同任务复杂度下涌现的智能体行为与算法权衡。通过利用JAX在GPU上实现硬件加速执行，TABX支持大规模并行化并显著降低计算开销。凭借快速、可扩展且易于定制的框架，TABX促进了复杂结构化领域中MARL智能体的研究，并为未来研究提供了可扩展的基础。代码已开源：https://github.com/ku-dmlab/TABX。