高吞吐多智能体强化学习仿真平台，支持快速定制复杂任务
TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning
- 基于JAX构建，支持GPU加速与大规模并行
- 可灵活配置环境参数，支持多样化任务设计
- 适合研究复杂协作行为与算法性能对比
环境设计对协作式多智能体强化学习（MARL）算法的发展与评估至关重要。现有基准虽揭示关键挑战，但往往缺乏模块化能力，难以定制化评估场景。我们提出完全加速的JAX战斗模拟器（TABX），一个高吞吐、可重构的多智能体任务沙盒。TABX提供环境参数的细粒度控制，可系统研究智能体涌现行为及算法在不同任务复杂度下的权衡。借助JAX实现硬件加速，支持GPU执行，显著降低计算开销。该框架具备高速、可扩展、易定制特性，适用于复杂结构化领域中MARL智能体的研究，并为未来研究提供可扩展基础。代码已开源：https://github.com/ku-dmlab/TABX。
原文摘要 · Abstract (English)
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.
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