The field of Multi-Agent Reinforcement Learning (MARL) is currently facing a reproducibility crisis. While solutions for standardized reporting have been proposed to address the issue, we still lack a benchmarking tool that enables standardization and reproducibility, while leveraging cutting-edge Reinforcement Learning (RL) implementations. In this paper, we introduce BenchMARL, the first MARL training library created to enable standardized benchmarking across different algorithms, models, and environments. BenchMARL uses TorchRL as its backend, granting it high performance and maintained state-of-the-art implementations while addressing the broad community of MARL PyTorch users. Its design enables systematic configuration and reporting, thus allowing users to create and run complex benchmarks from simple one-line inputs. BenchMARL is open-sourced on GitHub: https://github.com/facebookresearch/BenchMARL

翻译：多智能体强化学习（MARL）领域正面临可复现性危机。尽管已有针对标准化报告提出的解决方案，但我们仍缺乏一种能够实现标准化与可复现性、同时利用前沿强化学习（RL）实现的基准测试工具。本文介绍首个为跨算法、模型与环境实现标准化基准测试而创建的MARL训练库——BenchMARL。该库以TorchRL为后端，在服务广大PyTorch用户群体的同时，兼具高性能与持续维护的先进实现。其设计支持系统化配置与报告，用户可通过简单的一行输入创建并运行复杂基准测试。BenchMARL已在GitHub开源：https://github.com/facebookresearch/BenchMARL