BenchMARL: Benchmarking Multi-Agent Reinforcement Learning

BenchMARL execution diagram


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.

In Preprint
Matteo Bettini
Matteo Bettini
PhD Candidate

Matteo’s research is focused on studying heterogeneity and resilience in multi-agent and multi-robot systems.

Amanda Prorok
Amanda Prorok

Amanda’s research focuses on multi-agent and multi-robot systems. Our mission is to find new ways of coordinating artificially intelligent agents (e.g., robots, vehicles, machines) to achieve common goals in shared physical and virtual spaces.