We study the allocation of reconfigurable intelligent surfaces (RISs) in a multi-cell wireless network, where base stations compete for control of shared RIS units deployed at the cell edges. These RISs, provided by an independent operator, are dynamically leased to the highest bidder using a simultaneously ascending auction format. Each base station estimates the utility of acquiring additional RISs based on macroscopic channel parameters, enabling a scalable and low-overhead allocation mechanism. To optimize the bidding behavior, we integrate deep reinforcement learning (DRL) agents that learn to maximize performance while adhering to budget constraints. Through simulations in clustered cell-edge environments, we demonstrate that reinforcement learning (RL)-based bidding significantly outperforms heuristic strategies, achieving optimal trade-offs between cost and spectral efficiency. Furthermore, we introduce a tunable parameter that governs the bidding aggressiveness of RL agents, enabling a flexible control of the trade-off between network performance and expenditure. Our results highlight the potential of combining auction-based allocation with adaptive RL mechanisms for efficient and fair utilization of RISs in next-generation wireless networks.
DRL-AdaPart: DRL-Driven Adaptive STAR-RIS Partitioning for Fair and Efficient Resource Utilization
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Imperial College London（伦敦帝国学院）；University of Peradeniya（佩拉德尼亚大学）；University of Southampton（南安普顿大学）
A Heuristic-Integrated DRL Approach for Phase Optimization in Large-Scale RISs
Communication Systems and Networks Research Group, University of Bristol（布里斯托大学通信系统与网络研究组）；Bristol Research & Innovation Laboratory, Toshiba Europe Ltd.（布里斯托研究与创新实验室，东芝欧洲有限公司）
Deep Reinforcement Learning Based Joint Downlink Beamforming and RIS Configuration in RIS-aided MU-MISO Systems Under Hardware Impairments and Imperfect CSI
Bilkent University（比尔肯特大学）；KTH Royal Institute of Technology（KTH皇家理工学院）
Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design
Zhejiang University（浙江大学）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC联合学院）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里安大学）；University of Electronic Science and Technology of China（电子科技大学）；Singapore University of Technology and Design（新加坡科技设计大学）；CentraleSupélec（巴黎中央理工-高等电力学院）；University Paris-Saclay（巴黎萨克雷大学）
Domain-adapted Learning and Imitation: DRL for Power Arbitrage
University College London（伦敦大学学院）；Shell Global Solutions International（壳牌全球解决方案国际公司）；Shell Shared Service Center Chennai（壳牌金奈共享服务中心）；Shell Energy North America（壳牌能源北美公司）
Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics
Technische Universität Dresden（德累斯顿工业大学）；Oxford Brookes University（牛津布鲁克斯大学）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗森多夫亥姆霍兹中心）；University of Lisbon（里斯本大学）；Hunan University（湖南大学）
Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization
Information and Communications Technology Cluster, Singapore Institute of Technology（信息与通信技术集群，新加坡科技学院）；Department of Electrical and Computer Engineering, National University of Singapore（国立新加坡大学电子与计算机工程系）；Department of Electrical and Electronic Engineering, University of Hong Kong（香港大学电子与电气工程系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge
National University of Defense Technology（中国人民解放军国防科技大学）；Kings College London（伦敦国王学院）；Cranfield University（克兰菲尔德大学）；Alan Turing Institute（艾伦·图灵研究所）；Queen Mary University of London（伦敦玛丽女王大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives
College of Control Science and Engineering Zhejiang University（控制科学与工程学院浙江大学）；Department of Chemical Engineering Chung-Yuan Christian University（化学工程系 Chung-Yuan Christian University）
Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces
National and Kapodistrian University of Athens（雅典国立卡波季斯特里安大学）；Technology Innovation Institute（技术创新研究院）；Zhejiang University（浙江大学）；Singapore University of Technology and Design（新加坡科技设计大学）；University of Oulu（奥卢大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
