BenchMARL: Benchmarking Multi-Agent Reinforcement Learning
Matteo Bettini Amanda Prorok Vincent Moens
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