TABX：面向多智能体强化学习的高吞吐沙盒战斗模拟器
TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning
AI总结：
提出基于JAX的高吞吐沙盒模拟器TABX，通过可重构任务和硬件加速支持多智能体强化学习的高效研究与评估。
摘要：
环境的设计在塑造合作多智能体强化学习（MARL）算法的开发和评估中起着关键作用。虽然现有基准突出了关键挑战，但它们通常缺乏设计自定义评估场景所需的模块化。我们介绍了基于JAX的全加速战斗模拟器（TABX），这是一个专为可重构多智能体任务设计的高吞吐沙盒。TABX提供对环境参数的精细控制，允许系统地研究涌现的智能体行为和跨不同任务复杂度谱系的算法权衡。利用JAX在GPU上进行硬件加速执行，TABX实现了大规模并行化并显著降低了计算开销。通过提供一个快速、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，并作为未来研究的可扩展基础。我们的代码可在https://github.com/ku-dmlab/TABX获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
