Assistax: A Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics

Leonard Hinckeldey, Elliot Fosong, Elle Miller, Rimvydas Rubavicius, Trevor McInroe, Patricia Wollstadt, Christiane B. Wiebel-Herboth, Subramanian Ramamoorthy, Stefano V. Albrecht

Keywords: Reinforcement Learning, Multi-Agent Reinforcement Learning, Ad-hoc Teamwork, Zero-Shot Coordination

Abstract: The development of reinforcement learning (RL) algorithms has been largely driven by ambitious challenge tasks and benchmarks. Games have dominated RL benchmarks because they present relevant challenges, are inexpensive to run and easy to understand. While games such as Go and Atari have led to many breakthroughs, they often do not directly translate to real-world embodied applications. In recognising the need to diversify RL benchmarks and addressing complexities that arise in embodied interaction scenarios, we introduce Assistax: an open-source benchmark designed to address challenges arising in assistive robotics tasks. Assistax uses JAX’s hardware acceleration for significant speed-ups for learning in physics-based simulations. In terms of open-loop wall-clock time Assistax runs up to $370\times$ faster, compared to CPU-based alternatives, when vectorising training runs. Assistax conceptualises the interaction between an assistive robot and an active human patient using multi-agent RL to train a population of diverse partner agents against which an embodied robotic agent's zero-shot coordination capabilities can be tested. Extensive evaluation and hyperparameter tuning for popular continuous control RL and MARL algorithms provide reliable baselines and establish Assistax as a practical benchmark for advancing RL research for assistive robotics.

Submission Number: 25