“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.”

630

130

630

GitPedia Card178/002

VectorizedMultiAgentSimulator

proroklab

Pythongymgym-environment

“VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.”

MARL-papers-with-code

TimeBreaker

marlmarl-papers

“Multi-Agent Reinforcement Learning (MARL) papers with code”

Multi-agent-RL

Ronchy2000

Jupyter Notebookmaddpg-pytorchmarl

“Projects from basic algorithms to MARL. Implements MADDPG,MATD3,MA/HAPPO in Predator-Prey pursuit games with PettingZoo MPE environments.”

Multi-Agent-Reinforcement-Learning-papers

TimeBreaker

marlmulti-agent-learning

“Multi-Agent Reinforcement Learning (MARL) papers”

pogema

Cognitive-AI-Systems

Pythongym-environmentmapf

“[ICLR-2025] POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF / MAPF settings.”

MARL-resources-collection

TimeBreaker

marlmulti-agent-learning

“A Collection of Multi-Agent Reinforcement Learning (MARL) Resources”

craftium

mikelma

C++artificial-intelligencegymnasium

“A framework for creating rich, 3D, Minecraft-like single and multi-agent environments for AI research. (Accepted at ICML 2025).”

gym-continuousDoubleAuction

ChuaCheowHuan

Jupyter Notebookdouble-auctionfinancial-engineering

“A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.”

SQDDPG

hsvgbkhgbv

Pythonframeworkmarl

“This is a framework for the research on multi-agent reinforcement learning and the implementation of the experiments in the paper titled by ''Shapley Q-value: A Local Reward Approach to Solve Global Reward Games''.”