# Asynchronous Cooperative Multi-Agent Reinforcement Learning with Limited Communication

arXiv: https://arxiv.org/abs/2502.00558 · HTML: https://mlsanity.com/paper/2502.00558/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2025.

## Built on by (most cited first, 1 of 1)

- [Heterogeneous Information-Bottleneck Coordination Graphs for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.17393/) (2026)

## Abstract (arXiv)

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.