Abstract: In this paper, we introduce an approach based on Deep Reinforcement Learning to optimize the RIS-NOMA scheme for the 6G wireless network. Some other parameters considered as actions in the proposed algorithm are clustering of users, power distribution, and RIS phase optimization. The two types of deep reinforcement learning algorithms, namely, SAC and MADDPG, were implemented on the proposed architecture. The performance of the proposed framework has been examined based on some parameters including sum rate, SINR, throughput, delay, energy efficiency, and Jain's fairness index. The suggested MADDPG and SAC models have shown better performance than existing models, based on the obtained results..
Key Word: Deep Reinforcement Learning (DRL), Non-Orthogonal Multiple Access (NOMA), Soft Actor Critic (SAC), Reconfigurable Intelligent Surface (RIS).
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