mlsanityData to 14 Oct 2026

JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes

arXiv 2505.06771 ↗First posted 10 May 2025cs.RO cs.LG cs.MAcited by 1

On the map: Deep reinforcement learning methods → Multi-agent reinforcement learning

Abstract (arXiv)

Multi-agent reinforcement learning (MARL) has emerged as a promising solution for learning complex and scalable coordination behaviors in multi-robot systems. However, established MARL platforms (e.g., SMAC and MPE) lack robotics relevance and hardware deployment, leaving multi-robot learning researchers to develop bespoke environments and hardware testbeds dedicated to the development and evaluation of their individual contributions. The Multi-Agent RL Benchmark and Learning Environment for the Robotarium (MARBLER) is an exciting recent step in providing a standardized robotics-relevant platform for MARL, by bridging the Robotarium testbed with existing MARL software infrastructure. However, MARBLER lacks support for parallelization and GPU/TPU execution, making the platform prohibitively slow compared to modern MARL environments and hindering adoption. We contribute JaxRobotarium, a Jax-powered end-to-end simulation, learning, deployment, and benchmarking platform for the Robotarium. JaxRobotarium enables rapid training and deployment of multi-robot RL (MRRL) policies with realistic robot dynamics and safety constraints, supporting parallelization and hardware acceleration. Our generalizable learning interface integrates easily with SOTA MARL libraries (e.g., JaxMARL). In addition, JaxRobotarium includes eight standardized coordination scenarios, including four novel scenarios that bring established MARL benchmark tasks (e.g., RWARE and Level-Based Foraging) to a robotics setting. We demonstrate that JaxRobotarium retains high simulation fidelity while achieving dramatic speedups over baseline (20x in training and 150x in simulation), and provides an open-access sim-to-real evaluation pipeline through the Robotarium testbed, accelerating and democratizing access to multi-robot learning research and evaluation. Our code is available at https://github.com/GT-STAR-Lab/JaxRobotarium.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Multi-agent reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 27 of its 45 references that are in the map. First posted on arXiv 10 May 2025.

Builds on

27 of 45 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Generalization of Heterogeneous Multi-Robot Policies via Awareness and Communication of Capabilities23 Jan

Other references · 26

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  4. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  5. 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environmentscited by 295
  6. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 260
  7. 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 212
  8. 2019Multi-Agent Pathfinding: Definitions, Variants, and Benchmarkscited by 199
  9. 2020FACMAC: Factored Multi-Agent Centralised Policy Gradientscited by 178
  10. 2022SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learningcited by 122
  11. 2020Shared Experience Actor-Critic for Multi-Agent Reinforcement Learningcited by 116
  12. 2024Simplifying Deep Temporal Difference Learningcited by 78
14 more
  1. 2022VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learningcited by 68
  2. 2020Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planningcited by 43
  3. 2023Heterogeneous Multi-Robot Reinforcement Learningcited by 36
  4. 2023BenchMARL: Benchmarking Multi-Agent Reinforcement Learningcited by 33
  5. 2022Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workerscited by 30
  6. 2022Multi-agent Motion Planning from Signal Temporal Logic Specificationscited by 21
  7. 2019MuSHR: A Low-Cost, Open-Source Robotic Racecar for Education and Researchcited by 18
  8. 2022Transferring Multi-Agent Reinforcement Learning Policies for Autonomous Driving using Sim-to-Realcited by 17
  9. 2024The Cambridge RoboMaster: An Agile Multi-Robot Research Platformcited by 8
  10. 2019An Optimal Task Allocation Strategy for Heterogeneous Multi-Robot Systemscited by 6
  11. 2019Robust Barrier Functions for a Fully Autonomous, Remotely Accessible Swarm-Robotics Testbedcited by 4
  12. 2025Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordinationcited by 3
  13. 2022MultiRoboLearn: An open-source Framework for Multi-robot Deep Reinforcement Learningcited by 2
  14. 2025Evaluating and Improving Graph-based Explanation Methods for Multi-Agent Coordinationcited by 1

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Roboticscited by 0

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