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In this paper, the cooperative edge caching problem in fog radio accessnetworks (F-RANs) is investigated. To minimize the content transmission delay,we formulate the cooperative caching optimization problem to find the globallyoptimal caching strategy.By considering the non-deterministic polynomial hard(NP-hard) property of this problem, a Multi Agent Reinforcement Learning(MARL)-based cooperative caching scheme is proposed.Our proposed scheme appliesdouble deep Q-network (DDQN) in every fog access point (F-AP), and introducesthe communication process in multi-agent system. Every F-AP records thehistorical caching strategies of its associated F-APs as the observations ofcommunication procedure.By exchanging the observations, F-APs can leverage thecooperation and make the globally optimal caching strategy.Simulation resultsshow that the proposed MARL-based cooperative caching scheme has remarkableperformance compared with the benchmark schemes in minimizing the contenttransmission delay.

Cooperative Edge Caching via Multi Agent Reinforcement Learning in Fog Radio Access Networks