Multi-agent reinforcement learning (MARL) has achieved remarkable success in various challenging problems. Meanwhile, more and more benchmarks have emerged and provided some standards to evaluate the algorithms in different fields. On the one hand, the virtual MARL environments lack knowledge of real-world tasks and actuator abilities, and on the other hand, the current task-specified multi-robot platform has poor support for the generality of multi-agent reinforcement learning algorithms and lacks support for transferring from simulation to the real environment. Bridging the gap between the virtual MARL environments and the real multi-robot platform becomes the key to promoting the practicability of MARL algorithms. This paper proposes a novel MARL environment for real multi-robot tasks named NeuronsMAE (Neurons Multi-Agent Environment). This environment supports cooperative and competitive multi-robot tasks and is configured with rich parameter interfaces to study the multi-agent policy transfer from simulation to reality. With this platform, we evaluate various popular MARL algorithms and build a new MARL benchmark for multi-robot tasks. We hope that this platform will facilitate the research and application of MARL algorithms for real robot tasks. Information about the benchmark and the open-source code will be released.

翻译：多智能体强化学习（MARL）已在多种复杂问题中取得显著成功。与此同时，越来越多的基准测试涌现，为不同领域的算法评估提供了标准化准则。然而，现有虚拟MARL环境缺乏对真实世界任务及执行器能力的认知，且当前面向特定任务的多机器人平台对多智能体强化学习算法的通用性支持不足，并缺乏从仿真到真实环境迁移的能力。弥合虚拟MARL环境与真实多机器人平台之间的鸿沟，成为推动MARL算法实用化的关键。本文提出一种面向真实多机器人任务的新型MARL环境——NeuronsMAE（神经元多智能体环境）。该环境支持协作与竞争性多机器人任务，并配置了丰富的参数接口，用于研究从仿真到现实的多智能体策略迁移。借助这一平台，我们评估了多种主流MARL算法，并构建了面向多机器人任务的MARL新基准。我们期望该平台能促进MARL算法在真实机器人任务中的研究与应用。基准测试信息及开源代码将后续发布。