用强化学习+贪心算法优化大规模智能表面相位，提升无线信号覆盖。
A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs
- 在DDQN中累积多步动作，实现列级控制
- 每步引入贪心算法微调元素级配置
- 小动作空间下有效优化大规模RIS
大规模可重构智能表面（RIS）的离散相位优化因非凸、非线性特性而困难。本文提出一种融合启发式算法的深度强化学习（DRL）框架：（1）在双深度Q网络（DDQN）中利用多步累积动作实现RIS列级控制；（2）在每个DRL步骤中集成贪心算法（GA），通过细粒度的元素级优化精炼状态。通过包含GA的状态学习，该方法在小动作空间下有效实现了大规模RIS相位配置优化。
原文摘要 · Abstract (English)
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.
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