TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning
This provides a scalable and customizable framework for researchers in multi-agent reinforcement learning, though it is incremental as it builds on existing environment design concepts.
The authors tackled the lack of modular and high-throughput environments for evaluating cooperative multi-agent reinforcement learning algorithms by introducing TABX, a sandbox simulator that enables massive parallelization on GPUs, significantly reducing computational overhead.
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://anonymous.4open.science/r/TABX-00CA.