Communication is one of the effective means to improve the learning of cooperative policy in multi-agent systems. However, in most real-world scenarios, lossy communication is a prevalent issue. Existing multi-agent reinforcement learning with communication, due to their limited scalability and robustness, struggles to apply to complex and dynamic real-world environments. To address these challenges, we propose a generalized communication-constrained model to uniformly characterize communication conditions across different scenarios. Based on this, we utilize it as a learning prior to distinguish between lossy and lossless messages for specific scenarios. Additionally, we decouple the impact of lossy and lossless messages on distributed decision-making, drawing on a dual mutual information estimatior, and introduce a communication-constrained multi-agent reinforcement learning framework, quantifying the impact of communication messages into the global reward. Finally, we validate the effectiveness of our approach across several communication-constrained benchmarks.
@article{arxiv.2512.03528,
title = {Multi-Agent Reinforcement Learning with Communication-Constrained Priors},
author = {Guang Yang and Tianpei Yang and Jingwen Qiao and Yanqing Wu and Jing Huo and Xingguo Chen and Yang Gao},
journal= {arXiv preprint arXiv:2512.03528},
year = {2026}
}