A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs
원문 초록
Optimizing discrete phase shifts in large-scale reconfigurable intelligent surfaces (RISs) is challenging due to their non-convex and non-linear nature. In this letter, we propose a heuristic-integrated deep reinforcement learning (DRL) framework that (1) leverages accumulated actions over multiple steps in the double deep Q-network (DDQN) for RIS column-wise control and (2) integrates a greedy algorithm (GA) into each DRL step to refine the state via fine-grained, element-wise optimization of RIS configurations. By learning from GA-included states, the proposed approach effectively addresses RIS optimization within a small DRL action space, demonstrating its capability to optimize phase-shift configurations of large-scale RISs.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Deep Reinforcement Learning with Gradient Eligibility Traces
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Design
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments
Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
토론
이 논문과 연결된 포럼 글이 아직 없습니다.