Cooperative MARL With Compressed State Information for Transmission Scheduling in Underwater Acoustic Networks

ABSTRACT

Underwater acoustic (UWA) networks suffer from long propagation delays and highly dynamic channels, making transmission scheduling difficult. While cooperative multi‐agent reinforcement learning (MARL) can improve performance, its conventional learning framework requires a central scheduler (CS) to collect full state information from all agents, incurring heavy communication overhead especially costly in UWA environments. In this study, we propose a cooperative MARL framework with compressed state sharing. Each agent encodes its local history, that is, queue length, CSI, and past transmission strategy, using a deep learning model to generate a compact state representation, which is then sent to the CS. The CS uses the aggregated compressed state to guide the joint scheduling and power allocation of all the agents. Simulation results confirm that the proposed method significantly reduces communication overhead while preserving high scheduling performance.