Jingrui Su

ShellMean-MAPPO: A Conflict-Aware MARL Framework for Downlink Resource Allocation in Multi-Shell LEO Satellite Networks

This work proposes a structured multi-agent reinforcement learning (MARL) framework based on multi-agent proximal policy optimization (MAPPO), termed ShellMean-MAPPO, for downlink resource allocation with explicit conflict resolution, and demonstrates its advantages over representative MARL schemes in terms of scheduling performance and conflict mitigation.