  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.
