  This paper studies a multi-Intelligent Reflecting Surfaces (IRSs)-assisted
wireless network consisting of multiple base stations (BSs) serving a set of
mobile users. We focus on the IRS-BS association problem in which multiple BSs
compete with each other for controlling the phase shifts of a limited number of
IRSs to maximize the long-term downlink data rate for the associated users. We
propose MDLBI, a Multi-agent Deep Reinforcement Learning-based BS-IRS
association scheme that optimizes the BS-IRS association as well as the
phase-shift of each IRS when being associated with different BSs. MDLBI does
not require information exchanging among BSs. Simulation results show that
MDLBI achieves significant performance improvement and is scalable for large
networking systems.
