facebookresearch/BenchMARL
BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.
BenchMARL helps machine learning researchers compare different Multi-Agent Reinforcement Learning (MARL) algorithms. You input various algorithms, environments, and models, and it systematically generates standardized performance metrics and plots for fair comparison. It's designed for researchers evaluating their novel MARL approaches against existing solutions or exploring the current landscape of MARL techniques.
580 stars. Available on PyPI.
Use this if you need to reliably benchmark and compare the performance of different multi-agent reinforcement learning algorithms or models across various simulated environments.
Not ideal if you are looking for a general-purpose reinforcement learning library for single-agent tasks or if you don't require standardized, reproducible comparisons for research.
Stars
580
Forks
117
Language
Python
License
MIT
Category
Last pushed
Feb 07, 2026
Commits (30d)
0
Dependencies
6
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