TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning

2026-02-02

AI Summary

This work proposes the first JAX-based multi-agent reinforcement learning (MARL) sandbox framework that is highly parallel, scalable, and extensively customizable, addressing the lack of modularity in existing MARL benchmark environments. The framework enables fine-grained control and rapid configuration of environmental parameters, facilitating flexible construction and evaluation of user-defined scenarios. By leveraging GPU hardware acceleration, it supports large-scale parallel simulation, substantially improving experimental throughput and flexibility while significantly reducing computational overhead. This provides an efficient and reconfigurable foundation for advancing MARL algorithm research in complex, structured domains.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Reinforcement Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• multi-agent reinforcement learning
• benchmark environments
• modular design
• custom evaluation scenarios
• cooperative MARL

Innovation

• multi-agent reinforcement learning
• JAX
• high-throughput simulation
• sandbox environment
• hardware acceleration