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

TL;DR

MultiRoboLearn is an open-source framework that bridges multi-robot deep reinforcement learning algorithms with practical applications, offering standardized simulation and real-world deployment capabilities.

Contribution

It introduces a unified, scalable framework that facilitates easy transition from simulation to real-world multi-robot systems and provides benchmarking tools for RL algorithms.

Findings

Demonstrated framework's effectiveness in real-world scenarios

Showcased compatibility with various RL algorithms in discrete and continuous spaces

Validated scalability and generality of the framework

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 MultiRoboLearn1. 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 scenarios2 using different types of multi-agent deep reinforcement learning algorithms in discrete and continuous action spaces.