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.

本文介绍了一个用于多机器人强化学习的可扩展仿真平台SMART，该平台包含了一个仿真环境和一个真实的多机器人系统，以提供多样化的交互场景进行训练，并支持基于插件的算法实现。在此基础上，我们研究了合作驾驶变道场景所涉及到的若干具有挑战性的问题，并开源我们的仿真环境及其相关基准测试任务和最先进的基线模型，以推动和强化多机器人强化学习的研究。

从多智能体到多机器人：一款可扩展的多机器人强化学习训练和评估平台