We study the problem of user association, namely finding the optimal assignment of user equipment to base stations to achieve a targeted network performance. In this paper, we focus on the knowledge transferability of association policies. Indeed, traditional non-trivial user association schemes are often scenario-specific or deployment-specific and require a policy re-design or re-learning when the number or the position of the users change. In contrast, transferability allows to apply a single user association policy, devised for a specific scenario, to other distinct user deployments, without needing a substantial re-learning or re-design phase and considerably reducing its computational and management complexity. To achieve transferability, we first cast user association as a multi-agent reinforcement learning problem. Then, based on a neural attention mechanism that we specifically conceived for this context, we propose a novel distributed policy network architecture, which is transferable among users with zero-shot generalization capability i.e., without requiring additional training.Numerical results show the effectiveness of our solution in terms of overall network communication rate, outperforming centralized benchmarks even when the number of users doubles with respect to the initial training point.
Learning Transferable Policies from Action-free Time Series Through Dynamical Embeddings
Hector Institute for AI in Psychiatry (HITKIP)（赫克托人工智能精神病学研究所）；Central Institute of Mental Health (CIMH)（中央精神健康研究所）；Heidelberg University（海德堡大学）；Interdisciplinary Center for Scientific Computing (IWR)（跨学科科学计算中心）；Hertie Institute for AI in Brain Health（赫蒂人工智能脑健康研究所）；University of Tübingen（蒂宾根大学）
Zero-Shot Transferable Solution Method for Parametric Optimal Control Problems
Oden Institute, UT Austin（UT奥斯汀奥登研究所）；Department of Mathematics, UCLA（加州大学洛杉矶分校数学系）；Department of Mathematical and Statistical Sciences, Clemson University（克莱姆斯大学数学与统计学系）；Department of Civil and Systems Engineering, Johns Hopkins University（约翰霍普金斯大学土木与系统工程系）
Transferable Graphical MARL for Real-Time Estimation in Dynamic Wireless Networks
Department of Electrical and Computer Engineering, Rutgers University（罗格斯大学电气与计算机工程系）；Department of Biostatistics and Bioinformatics, Duke University（杜克大学生物统计与生物信息学系）；Department of Electrical and Systems Engineering, University of Pennsylvania（宾夕法尼亚大学电气与系统工程系）
Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation
Cyber Defence Lab, CISS Department Royal Military Academy（国防网络安全实验室，信息与系统科学系皇家军事学院）；AI Lab, Department of Computer Science Vrije Universiteit Brussel（人工智能实验室，计算机科学系自由大学布鲁塞尔）
Towards a more efficient computation of individual attribute and policy contribution for post-hoc explanation of cooperative multi-agent systems using Myerson values
Artificial and Natural Intelligence Toulouse Institute (ANITI)（图卢兹人工与自然智能研究所）；University of Toulouse（图卢兹大学）；ISAE-Supaero（法国高等航空航天学院）；Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI)（安达卢西亚数据科学与计算智能研究所）；Research Center for Information and Communications Technologies of the University of Granada (CITIC-UGR)（格拉纳达大学信息与通信技术研究中心）；University of Granada（格拉纳达大学）
