JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes
Shalin Anand Jain, Jiazhen Liu, Siva Kailas, Harish Ravichandar

TL;DR
JaxRobotarium is a fast, scalable, and robotics-relevant platform for multi-robot reinforcement learning, enabling rapid training, deployment, and benchmarking with realistic dynamics and safety constraints.
Contribution
It introduces JaxRobotarium, a GPU-accelerated, end-to-end simulation and deployment platform that supports parallelization and integrates with state-of-the-art MARL libraries for robotics.
Findings
Achieves 20x faster training and 150x faster simulation than baseline.
Supports realistic robot dynamics and safety constraints.
Includes eight standardized multi-robot coordination scenarios.
Abstract
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…
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Taxonomy
TopicsGenetics, Bioinformatics, and Biomedical Research · Scientific Computing and Data Management
