{"ID":2833357,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.03528","arxiv_id":"2512.03528","title":"Multi-Agent Reinforcement Learning with Communication-Constrained Priors","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2512.03528","url_pdf":"https://arxiv.org/pdf/2512.03528v3","authors":"[\"Guang Yang\",\"Tianpei Yang\",\"Jingwen Qiao\",\"Yanqing Wu\",\"Jing Huo\",\"Xingguo Chen\",\"Yang Gao\"]","published":"2025-12-03T07:35:07Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.MA\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}