In sparse reward scenarios of reinforcement learning (RL), the memory mechanism provides promising shortcuts to policy optimization by reflecting on past experiences like humans. However, current memory-based RL methods simply store and reuse high-value policies, lacking a deeper refining and filtering of diverse past experiences and hence limiting the capability of memory. In this paper, we propose AdaMemento, an adaptive memory-enhanced RL framework. Instead of just memorizing positive past experiences, we design a memory-reflection module that exploits both positive and negative experiences by learning to predict known local optimal policies based on real-time states. To effectively gather informative trajectories for the memory, we further introduce a fine-grained intrinsic motivation paradigm, where nuances in similar states can be precisely distinguished to guide exploration. The exploitation of past experiences and exploration of new policies are then adaptively coordinated by ensemble learning to approach the global optimum. Furthermore, we theoretically prove the superiority of our new intrinsic motivation and ensemble mechanism. From 59 quantitative and visualization experiments, we confirm that AdaMemento can distinguish subtle states for better exploration and effectively exploiting past experiences in memory, achieving significant improvement over previous methods.
Intelligent Hybrid Resource Allocation in MEC-assisted RAN Slicing Network
School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；Purple Mountain Laboratories（紫金山实验室）；Singapore University of Technology and Design（新加坡科技设计大学）
Sum Rate Maximization in STAR-RIS-UAV-Assisted Networks: A CA-DDPG Approach for Joint Optimization
School of Computer and Electronic Information, Guangxi University（广西大学计算机与电子信息学院）；College of Mathematics and Computer Science, Zhejiang A&F University（浙江农林大学数学与计算机科学学院）；School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）
Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning
Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Republic of Korea（计算机科学与工程系，成均馆大学，苏旺，韩国）；Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea（人工智能系，成均馆大学，苏旺，韩国）
Securing SIM-Assisted Wireless Networks via Quantum Reinforcement Learning
Smart Green Transformation Center, VinUniversity（Vin大学智能绿色转型中心）；Université Paris-Saclay - CNRS - CentraleSupélec - L2S（巴黎萨克雷大学 - CNRS - 中央超导实验室 - L2S）；School of Electrical and Data Engineering, University of Technology Sydney（悉尼技术大学电气与数据工程学院）；School of Computer Science and Statistics, Trinity College Dublin, The University of Dublin（都柏林大学三一学院计算机科学与统计学学院）
DRL-Enabled Trajectory Planing for UAV-Assisted VLC: Optimal Altitude and Reward Design
Department of Information and Communication Engineering, Tongji University（信息与通信工程系，同济大学）；Department of Electronic Engineering, Shanghai Jiao Tong University（电子工程系，上海交通大学）；Shenzhen International Graduate School, Tsinghua University（深圳国际研究生院，清华大学）
UAV-Assisted Resilience in 6G and Beyond Network Energy Saving: A Multi-Agent DRL Approach
National Economics University（国立经济大学）；Norwegian University of Science and Technology（挪威科技大学）；Waseda University（早稻田大学）；Duy Tan University（杜维谭大学）；University of Luxembourg（卢森堡大学）；National Academy of Education Management（国家教育管理学院）
Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach
University of Electronic Science and Technology of China（电子科技大学）；Institute for Infocomm Research, Agency for Science, Technology and Research（新加坡科技研究局信息通信研究院）
DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
The University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Texas A&M University at Corpus Christi（德克萨斯农工大学科珀斯克里斯蒂分校）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Reducing Action Space: Reference-Model-Assisted Deep Reinforcement Learning for Inverter-based Volt-Var Control
Tsinghua-Berkeley Shenzhen Institute, Tsinghua University（清华大学清华-伯克利深圳学院）；Shanghai University of Electric Power（上海电力大学）；Tsinghua University（清华大学）；Beihang University（北京航空航天大学）
Federated Deep Reinforcement Learning for Energy Efficient Multi-Functional RIS-Assisted Low-Earth Orbit Networks
Department of Communication Engineering, National Central University, Taoyuan, Taiwan（国立中央大学通信工程系）；Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan（阳明交通大学电子工程系）；Next Generation Wireless, University of Southampton, Southampton, UK（南安普顿大学下一代无线技术）；Next-generation Communications Research Center, Hon Hai Research Institute, Taipei, Taiwan（富士康研究所下一代通讯研究中心）
Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning
Xi’an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences（中国科学院西安光学精密机械研究所）；Durham University（杜伦大学）；Jinan University（暨南大学）；The University of Manchester（曼彻斯特大学）
Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学电子工程系、北京信息科学与技术国家研究中心）；State Key Lab. of ISN and School of Telecommunications Engineering, Xidian University（西安电子科技大学信息与通信工程学院）；Qualcomm（高通）；School of Engineering, University of Kent（肯特大学工程学院）
Force-Modulated Visual Policy for Robot-Assisted Dressing with Arm Motions
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Department of Engineering Design, Indian Institute of Technology, Madras（印度理工学院Madras分校工程设计系）
Energy-Efficient UAV-assisted LoRa Gateways: A Multi-Agent Optimization Approach
College of Computing, Mohammed VI Polytechnic University (UM6P)（穆莱·易斯六世理工学院计算机学院）；Department of Computer Science, Khalifa University（哈利法大学计算机科学系）；Concordia Institute for Information Systems Engineering, Concordia University（康科迪亚大学信息系统工程研究所）
From See to Shield: ML-Assisted Fine-Grained Access Control for Visual Data
Abo Akademi University, Nokia Bell Labs, Finland（阿博阿卡迪米大学、诺基亚贝尔实验室，芬兰）；Linköping University, Nokia Bell Labs, Sweden（林肯皮大学、诺基亚贝尔实验室，瑞典）；Nokia Bell Labs, Finland（诺基亚贝尔实验室，芬兰）
