Asynchronous Cooperative Multi-Agent Reinforcement Learning with Limited Communication

Authors: Sydney Dolan*, Siddharth Nayak*, Jasmine Jerry Aloor, Hamsa Balakrishnan

We consider the problem setting in which multiple autonomous agents must cooperatively navigate and perform tasks in an unknown, communication-constrained environment. Traditional multi-agent reinforcement learning (MARL) approaches assume synchronous communications and perform poorly in such environments. We propose AsynCoMARL, an asynchronous MARL approach that uses graph transformers to learn communication protocols from dynamic graphs. AsynCoMARL can accommodate infrequent and asynchronous communications between agents, with edges of the graph only forming when agents communicate with each other. We show that AsynCoMARL achieves similar success and collision rates as leading baselines, despite 26\% fewer messages being passed between agents. [PDF], [Website]

Note: Code will be made public soon.

@article{dolan2025asynchronous,
  title={Asynchronous Cooperative Multi-Agent Reinforcement Learning with Limited Communication},
  author={Dolan, Sydney and Nayak, Siddharth and Aloor, Jasmine Jerry and Balakrishnan, Hamsa},
  journal={arXiv preprint arXiv:2502.00558},
  year={2025}
}