MultiRoboLearn: An open-source Framework for Multi-robot Deep Reinforcement Learning

Junfeng Chen, Fuqin Deng, Yuan Gao, Junjie Hu, Xiyue Guo, Guanqi Liang, Tin Lun Lam

Abstract: It is well known that it is difficult to have a reliable and robust framework to link multi-agent deep reinforcement learning algorithms with practical multi-robot applications. To fill this gap, we propose and build an open-source framework for multi-robot systems called MultiRoboLearn 1 . This framework builds a unified setup of simulation and real-world applications. It aims to provide standard, easy-to-use simulated scenarios that can also be easily deployed to real-world multi-robot environments. Also, the framework provides researchers with a benchmark system for comparing the performance of different reinforcement learning algorithms. We demonstrate the generality, scalability, and capability of the framework with two real-world scenarios using different types of multi-agent deep reinforcement learning algorithms in discrete and continuous action spaces.