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

• URL: http://arxiv.org/abs/2602.01665v1
• Date: Mon, 02 Feb 2026 05:34:38 GMT
• Title: TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning
• Authors: Hayeong Lee, JunHyeok Oh, Byung-Jun Lee,
• Abstract summary: We introduce the Totally Accelerated Battle Simulator in JAX (TABX) for reconfigurable multi-agent tasks.<n>TABX enables massive parallelization and significantly reduces computational overhead.<n>By providing a fast, scalable, and easily customized framework, TABX serves as a scalable foundation for future research.
• Score: 4.254850120280717
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• PymooLab: An Open-Source Visual Analytics Framework for Multi-Objective Optimization using LLM-Based Code Generation and MCDM [0.0]
  PymooLab is an open-source visual analytics environment built on top of textitpymoo.<n>It unifies configuration, execution monitoring, and formal decision support in a single reproducible workflow.<n>For computationally intensive studies, PymooLab relies on the native textitpymoo acceleration pathway through JAX.
  arXiv  Detail & Related papers  (2026-03-02T00:56:32Z)
• MARTI-MARS$^2$: Scaling Multi-Agent Self-Search via Reinforcement Learning for Code Generation [64.2621682259008]
  Multi-Agent Reinforced Training and Inference Framework with Self-Search Scaling (MARTI-MARS2)<n>We propose a Multi-Agent Reinforced Training and Inference Framework with Self-Search Scaling (MARTI-MARS2) to integrate policy learning with multi-agent tree search.<n>We show that MARTI-MARS2 achieves 77.7%, outperforming strong baselines like GPT-5.1 on challenging code generation benchmarks.
  arXiv  Detail & Related papers  (2026-02-08T07:28:44Z)
• Multi-Agent Craftax: Benchmarking Open-Ended Multi-Agent Reinforcement Learning at the Hyperscale [53.08403177911567]
  textitCraftax-MA is an extension of the popular open-ended RL environment, Craftax.<n>textitCraftax-Coop introduces heterogeneous agents, trading and more mechanics that require complex cooperation among agents for success.
  arXiv  Detail & Related papers  (2025-11-07T01:09:36Z)
• AgentGit: A Version Control Framework for Reliable and Scalable LLM-Powered Multi-Agent Systems [7.408263799616532]
  We present AgentGit, a framework that brings Git-like rollback and branching to multi-agent systems (MAS)<n>We show that AgentGit significantly reduces redundant, runtime and token usage, and supports parallel exploration across multiple branches.<n>This work offers a practical path to more robust MAS design and enables error recovery, safe exploration, computation, and A/B testing in collaborative AI systems.
  arXiv  Detail & Related papers  (2025-11-01T17:11:31Z)
• Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
  We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
  arXiv  Detail & Related papers  (2025-10-11T18:11:09Z)
• InternBootcamp Technical Report: Boosting LLM Reasoning with Verifiable Task Scaling [71.37579508777843]
  Large language models (LLMs) have revolutionized artificial intelligence by enabling complex reasoning capabilities.<n>To address this gap, we present InternBootcamp, an open-source framework comprising 1000+ domain-diverse task environments.
  arXiv  Detail & Related papers  (2025-08-12T05:00:00Z)
• MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning [46.286941981339304]
  We introduce MEAL, the first benchmark tailored for continual multi-agent reinforcement learning (CMARL)<n>Existing CL benchmarks run environments on the CPU, leading to computational bottlenecks and limiting the length of task sequences.<n>MEAL leverages JAX for GPU acceleration, enabling continual learning across sequences of 100 tasks on a standard desktop PC in a few hours.
  arXiv  Detail & Related papers  (2025-06-17T21:50:04Z)
• Multi-Agent Environments for Vehicle Routing Problems [1.0179489519625304]
  We propose a library composed of multi-agent environments that simulates classic vehicle routing problems. The library, built on PyTorch, provides a flexible modular architecture design that allows easy customization and incorporation of new routing problems.
  arXiv  Detail & Related papers  (2024-11-21T18:46:23Z)
• Very Large-Scale Multi-Agent Simulation in AgentScope [112.98986800070581]
  We develop new features and components for AgentScope, a user-friendly multi-agent platform. We propose an actor-based distributed mechanism towards great scalability and high efficiency. We also provide a web-based interface for conveniently monitoring and managing a large number of agents.
  arXiv  Detail & Related papers  (2024-07-25T05:50:46Z)
• TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation [41.21899915378596]
  We propose a multi-agent framework based on dynamic Task Decomposition and Agent Generation (TDAG)<n>This framework dynamically decomposes complex tasks into smaller subtasks and assigns each to a specifically generated subagent.<n>ItineraryBench is designed to assess agents' abilities in memory, planning, and tool usage across tasks of varying complexity.
  arXiv  Detail & Related papers  (2024-02-15T18:27:37Z)
• JaxMARL: Multi-Agent RL Environments and Algorithms in JAX [105.343918678781]
  We present JaxMARL, the first open-source, Python-based library that combines GPU-enabled efficiency with support for a large number of commonly used MARL environments. Our experiments show that, in terms of wall clock time, our JAX-based training pipeline is around 14 times faster than existing approaches. We also introduce and benchmark SMAX, a JAX-based approximate reimplementation of the popular StarCraft Multi-Agent Challenge.
  arXiv  Detail & Related papers  (2023-11-16T18:58:43Z)
• MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning [61.28547338576706]
  Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms. We present MALib, a scalable and efficient computing framework for PB-MARL.
  arXiv  Detail & Related papers  (2021-06-05T03:27:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.