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In this paper, we explore various multi-agent reinforcement learning (MARL)techniques to design grant-free random access (RA) schemes for low-complexity,low-power battery operated devices in massive machine-type communication (mMTC)wireless networks. We use value decomposition networks (VDN) and QMIXalgorithms with parameter sharing (PS) with centralized training anddecentralized execution (CTDE) while maintaining scalability. We then comparethe policies learned by VDN, QMIX, and deep recurrent Q-network (DRQN) andexplore the impact of including the agent identifiers in the observationvector. We show that the MARL-based RA schemes can achieve a betterthroughput-fairness trade-off between agents without having to condition on theagent identifiers. We also present a novel correlated traffic model, which ismore descriptive of mMTC scenarios, and show that the proposed algorithm caneasily adapt to traffic non-stationarities

Learning Random Access Schemes for Massive Machine-Type Communication with MARL