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Utilizing Deep Reinforcement Learning (DRL) for Reconfigurable IntelligentSurface (RIS) assisted wireless communication has been extensively researched.However, existing DRL methods either act as a simple optimizer or only solveproblems with concurrent Channel State Information (CSI) represented in thetraining data set. Consequently, solutions for RIS-assisted wirelesscommunication systems under time-varying environments are relativelyunexplored. However, communication problems should be considered with realisticassumptions; for instance, in scenarios where the channel is time-varying, thepolicy obtained by reinforcement learning should be applicable for situationswhere CSI is not well represented in the training data set. In this paper, weapply Meta-Reinforcement Learning (MRL) to the joint optimization problem ofactive beamforming at the Base Station (BS) and phase shift at the RIS,motivated by MRL's ability to extend the DRL concept of solving one MarkovDecision Problem (MDP) to multiple MDPs. We provide simulation results tocompare the average sum rate of the proposed approach with those of selectedforerunners in the literature. Our approach improves the sum rate by more than60% under time-varying CSI assumption while maintaining the advantages oftypical DRL-based solutions. Our study's results emphasize the possibility ofutilizing MRL-based designs in RIS-assisted wireless communication systemswhile considering realistic environment assumptions.

An MRL-Based Design Solution for RIS-Assisted MU-MIMO Wireless System under Time-Varying Channels