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）领域当前正面临可重复性危机。尽管已有标准化报告的解决方案被提出以应对该问题，我们仍缺少一个既能实现标准化与可重复性、又能利用前沿强化学习（RL）实现的基准测试工具。本文提出BenchMARL——首个旨在实现跨不同算法、模型与环境的标准化基准测试的MARL训练库。BenchMARL以TorchRL为后端，在兼顾高性能与维护最先进实现的同时，服务于广泛的MARL PyTorch用户群体。其设计支持系统性配置与报告，使用户能够通过简单的单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