From Multi-agent to Multi-robot: A Scalable Training and Evaluation Platform for Multi-robot Reinforcement Learning

TL;DR

This paper presents SMART, a scalable platform combining simulation and real-world multi-robot systems for comprehensive evaluation of multi-robot reinforcement learning methods, addressing a gap in real-world performance assessment.

Contribution

The paper introduces SMART, a novel platform integrating simulation and real-world testing for MRRL, with plug-and-play APIs and open-source resources to advance research.

Findings

Demonstrated SMART's effectiveness through a cooperative driving case study.

Identified unique challenges in MRRL not previously addressed.

Provided open-source tools and benchmarks to facilitate future research.

Abstract

Multi-agent reinforcement learning (MARL) has been gaining extensive attention from academia and industries in the past few decades. One of the fundamental problems in MARL is how to evaluate different approaches comprehensively. Most existing MARL methods are evaluated in either video games or simplistic simulated scenarios. It remains unknown how these methods perform in real-world scenarios, especially multi-robot systems. This paper introduces a scalable emulation platform for multi-robot reinforcement learning (MRRL) called SMART to meet this need. Precisely, SMART consists of two components: 1) a simulation environment that provides a variety of complex interaction scenarios for training and 2) a real-world multi-robot system for realistic performance evaluation. Besides, SMART offers agent-environment APIs that are plug-and-play for algorithm implementation. To illustrate the practicality of our platform, we conduct a case study on the cooperative driving lane change scenario. Building off the case study, we summarize several unique challenges of MRRL, which are rarely considered previously. Finally, we open-source the simulation environments, associated benchmark tasks, and state-of-the-art baselines to encourage and empower MRRL research.