CHARME: A chain-based reinforcement learning approach for the minor embedding problem
Department of Computer and Information Science and Engineering, University of Florida（计算机与信息科学与工程系，佛罗里达大学）；Theoritical Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室理论部）
Hoang M. Ngo, Nguyen H K. Do, Minh N. Vu, Tre' R. Jeter, Tamer Kahveci, My T. Thai
Quantum annealing (QA) has great potential to solve combinatorial optimization problems efficiently. However, the effectiveness of QA algorithms is heavily based on the embedding of problem instances, represented as logical graphs, into the quantum processing unit (QPU) whose topology is in the form of a limited connectivity graph, known as the minor embedding problem. Because the minor embedding problem is an NP-hard problem~\mbox{\cite{Goodrich2018}}, existing methods for the minor embedding problem suffer from scalability issues when faced with larger problem sizes. In this paper, we propose a novel approach utilizing Reinforcement Learning (RL) techniques to address the minor embedding problem, named CHARME. CHARME includes three key components: a Graph Neural Network (GNN) architecture for policy modeling, a state transition algorithm that ensures solution validity, and an order exploration strategy for effective training. Through comprehensive experiments on synthetic and real-world instances, we demonstrate the efficiency of our proposed order exploration strategy as well as our proposed RL framework, CHARME. In particular, CHARME yields superior solutions in terms of qubit usage compared to fast embedding methods such as Minorminer and ATOM. Moreover, our method surpasses the OCT-based approach, known for its slower runtime but high-quality solutions, in several cases. In addition, our proposed exploration enhances the efficiency of the training of the CHARME framework by providing better solutions compared to the greedy strategy.
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
Beihang University（北京航空航天大学）；Zhongguancun Laboratory（中关村实验室）；Institute of Data Space, Hefei Comprehensive National Science Center（合肥综合性国家科学中心数据空间研究院）；Institute of Artificial Intelligence, Peking University（北京大学人工智能研究院）；Nanyang Technological University（南洋理工大学）
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
Stanford University（斯坦福大学）
为了在不确定性下做出好的决策，智能体必须从观察中学习。要做到这一点，两个最常见的框架是上下文强盗和马尔可夫决策过程（MDP）。在本文中，我们研究是否存在算法的更一般的框架（MDP），自动提供最佳的性能界限的具体问题在手，无需用户干预，无需修改算法。特别是，它被发现，最近提出的MDP的强化学习算法的一个非常小的变化已经匹配的最佳可能的遗憾界$\tilde O (\sqrt{SAT})$在主导的术语，如果部署在一个表格上下文强盗问题，尽管代理不可知这样的设置。
Reinforcement Learning with Success Induced Task Prioritization
Moscow Institute of Physics and Technology（莫斯科物理技术学院）；AIRI；Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences（俄罗斯科学院联邦研究中心“计算机科学与控制”）
LearnAlign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment
Nanjing University（南京大学）；The Hong Kong Polytechnic University（香港理工大学）；The Chinese University of Hong Kong（香港中文大学）；University of Science and Technology of China（中国科学技术大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach
Beijing Key Laboratory of Security and Privacy in Intelligent Transportation（北京智能交通安全与隐私重点实验室）；Beijing Jiaotong University（北京交通大学）；College of Computing and Data Science（计算与数据科学学院）；Nanyang Technological University（南洋理工大学）；School of Computer Science and Technology（计算机科学与技术学院）；Taiyuan University of Technology（太原科技大学）；School of Electronics and Information Engineering（电子与信息工程学院）
DISCO-TAB: A Hierarchical Reinforcement Learning Framework for Privacy-Preserving Synthesis of Complex Clinical Data
Computational Science Research Center, San Diego State University（圣地亚哥州立大学计算科学研究中心）；School of Nursing and Department of Computer Science, University of California, Irvine（加州大学尔湾分校护理学院与计算机科学系）；Fowler College of Business, San Diego State University（圣地亚哥州立大学福勒商学院）；Department of Computer Science, San Diego State University（圣地亚哥州立大学计算机科学系）
High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning
TUAREG, Universitat Politècnica de Catalunya (UPC)（TUAREG，西班牙巴塞罗那理工大学）；Mechanical Engineering, Delft University of Technology (TU Delft)（代尔夫特理工大学机械工程系）；CASE, Barcelona Supercomputing Center (BSC)（巴塞罗那超级计算中心）
PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing
Department of Statistics, University of Michigan, USA（统计学系，密歇根大学）；Department of Biostatistics, University of Michigan, USA（生物统计学系，密歇根大学）；Department of Statistics, London School of Economics, UK（统计学系，伦敦经济学院）；Department of Health Behavior and Health Equity, School of Public Health, University of Michigan, USA（健康行为与健康公平系，公共卫生学院，密歇根大学）
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection
Department of Computer Sciences, University of Liverpool（计算机科学系，利物浦大学）；Centre for Doctoral Training (CDT) in Distributed Algorithm, University of Liverpool（分布式算法博士培训中心，利物浦大学）；Department of Electrical Engineering and Electronics, University of Liverpool（电子工程与电子系，利物浦大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）
