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.

翻译：众所周知,很难有一个可靠和有力的框架将多试剂深层强化学习算法与实用的多机器人应用法联系起来。为填补这一空白,我们提议并建立一个称为多机器人系统的开放源码框架,称为多机器人系统多RoboLearn1。这个框架建立一个统一的模拟和现实世界应用设置,目的是提供标准、易于使用的模拟假设情景,这些情景也可以很容易地部署到现实世界多机器人环境。此外,这个框架为研究人员提供了一个基准系统,用于比较不同强化学习算法的性能。我们用两种现实世界情景来展示框架的普遍性、可扩展性和能力,2 使用不同类型多试剂深度强化学习算法,在离散和连续的行动空间进行。