Multi-Agent Model-Based Reinforcement Learning for Decentralized Spectrum Sharing in Low-Altitude Economy

Abstract

Rapid advances in drone technology, combined with the growing congestion of terrestrial transport networks, are driving the emergence of the low-altitude economy. Uncrewed Aerial Vehicles (UAVs) are increasingly deployed for low-altitude economy applications such as urban logistics and transportation, yet their expansion is constrained by the scarcity of spectrum resources. Although Multi-Agent Reinforcement Learning (MARL) offers a promising decentralized approach to improve spectral efficiency of UAVs, existing MARL methods suffer from high training costs, often requiring extensive environmental interactions. To overcome these limitations, we propose a novel Multi-Agent Model-Based reinforcement learning algorithm for decentralized spectrum sharing among UAVs in the low-altitude economy, which we denote as MAMBA-UAV. Adopting a Centralized Training with Decentralized Execution (CTDE) paradigm, MAMBA-UAV equips each UAV with a learned world model that captures compact environmental representations and predicts system dynamics. These world models are then utilized during MARL training to simulate interactions, thereby reducing the reliance on repeated real-environment rollouts. Through comprehensive simulations, we demonstrate that MAMBA-UAV substantially reduces the number of environmental interactions required for UAVs to achieve competitive spectrum-sharing performance, lowering training costs while maintaining high performance.