Multi-Agent Reinforcement Learning with Communication-Constrained Priors

• URL: http://arxiv.org/abs/2512.03528v1
• Date: Wed, 03 Dec 2025 07:35:07 GMT
• Title: Multi-Agent Reinforcement Learning with Communication-Constrained Priors
• Authors: Guang Yang, Tianpei Yang, Jingwen Qiao, Yanqing Wu, Jing Huo, Xingguo Chen, Yang Gao,
• Abstract summary: Communication is one of the effective means to improve the learning of cooperative policy in multi-agent systems.<n>Existing multi-agent reinforcement learning with communication struggles to apply to complex and dynamic real-world environments.<n>We introduce a communication-constrained multi-agent reinforcement learning framework, quantifying the impact of communication messages into the global reward.
• Score: 22.124940712335434
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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