This is the pre-proceedings for the RLC 2026. You may expect minor changes.
Reinforcement Learning Journal, vol. 7, 2026, pp. TBD.
Will be presented at the Reinforcement Learning Conference (RLC), Montréal, Quebec, Canada, August 15–17, 2026.
Keywords: Multi-Agent Reinforcement Learning, Ad-Hoc Teamwork, Zero-Shot Coordination,
As embodied autonomous systems capable of assisting humans in daily activities continue to be a major goal for the field of robotics, it becomes increasingly important to have efficient and appropriate reinforcement learning (RL) simulation testbeds. Many common RL environments are too simple to provide insight into complex robotics domains, and many robotics simulations have throughput that is too low for RL applications. In particular, very few robotic simulation environments target multi-agent interactions. While most simulators treat the robot as an isolated agent, real-world tasks such as home assistance and caretaking are inherently multi-agent. Assistax addresses these limitations by providing a high-throughput, scalable suite of GPU-accelerated assistive robotics tasks built on JAX and MuJoCo-MJX, while also including an active humanoid agent serving as a simulated human partner that can be trained alongside the robot using multi-agent RL (MARL). Beyond the application of Assistax as a MARL benchmark environment, we formulate the interaction between the human and the robot as an Ad-Hoc Teamwork (AHT) problem, where the robot's policy must generalise to unseen humans with varying disabilities and preferences. To this end, we provide an extensive AHT benchmarking pipeline: we use MARL to pre-train a diverse population of humanoid partners, and evaluate the ability of the robot policies to coordinate with a withheld set of humanoid policies. In contrast to other benchmarks, we also release reactive MARL-pre-trained humanoid policies via Hugging Face, enabling faster iteration in AHT research. By leveraging hardware acceleration, Assistax achieves up to 412$\times$ faster open-loop simulation than comparable CPU-based environments, all on a single GPU. Our AHT pipeline reveals a coordination gap when we evaluate existing RL algorithms on a set of unseen partners with novel preference combinations. This RL-native test suite for embodied multi-agent interaction provides a practical benchmark for advancing RL in assistive care. The code is readily available on [GitHub](https://github.com/assistive-autonomy/assistax).
Leonard Hinckeldey, Elliot Fosong, Rimvydas Rubavicius, Elle Miller, Trevor McInroe, Fan Zhang, Patricia Wollstadt, Stefano V. Albrecht, and Subramanian Ramamoorthy. "Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics." Reinforcement Learning Journal, vol. 7, 2026, pp. TBD.
BibTeX:@article{hinckeldey2026assistax,
title={Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics},
author={Leonard Hinckeldey and Elliot Fosong and Rimvydas Rubavicius and Elle Miller and Trevor McInroe and Fan Zhang and Patricia Wollstadt and Stefano V. Albrecht and Subramanian Ramamoorthy},
journal={Reinforcement Learning Journal},
volume={7},
pages={},
year={2026}
}