Asynchronous Cooperative Multi-Agent Reinforcement Learning with Limited Communication
For multi-agent systems operating in communication-constrained environments, this work reduces communication overhead while maintaining performance.
AsynCoMARL enables cooperative multi-agent navigation under limited, asynchronous communication, achieving similar success and collision rates as baselines with 26% fewer messages.
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.