Offline reinforcement learning (RL) has progressed with return-conditioned supervised learning (RCSL), but its lack of stitching ability remains a limitation. We introduce $Q$-Aided Conditional Supervised Learning (QCS), which effectively combines the stability of RCSL with the stitching capability of $Q$-functions. By analyzing $Q$-function over-generalization, which impairs stable stitching, QCS adaptively integrates $Q$-aid into RCSL's loss function based on trajectory return. Empirical results show that QCS significantly outperforms RCSL and value-based methods, consistently achieving or exceeding the maximum trajectory returns across diverse offline RL benchmarks.
CDCP: Conditional Diffusion Model with Contextual Prompts for Multi-task Offline Safe Reinforcement Learning
Tongji University（同济大学）；JD Explore Academy（京东探索研究院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络科学与技术学院）；Berkeley AI Research Lab, University of California, Berkeley（加州大学伯克利分校伯克利人工智能研究实验室）；Technical University of Munich（慕尼黑工业大学）
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
Columbia University（哥伦比亚大学）；Northwestern University（西北大学）；Yale University（耶鲁大学）；University of Michigan（密歇根大学）；University of Washington（华盛顿大学）；University of Southern California（南加州大学）
NFTR: From Provable Mode-Averaging to Geodesic Subgoal Selection in Offline Goal-Conditioned RL
Huazhong University of Science and Technology（华中科技大学）；Xi’an Jiaotong University（西安交通大学）；University of Electronic Science and Technology of China（电子科技大学）
Is Conditional Generative Modeling all you need for Decision-Making?
Massachusetts Institute of Technology（麻省理工学院）；Improbable AI Lab（Improbable AI实验室）；Operations Research Center（运筹学中心）；Computer Science and Artificial Intelligence Lab（计算机科学与人工智能实验室）
A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）
Manifold-Constrained Energy-Based Transition Models for Offline Reinforcement Learning
Department of Electrical and Computer Engineering, George Washington University（乔治华盛顿大学电气与计算机工程系）；Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系）
MAGE: Multi-scale Autoregressive Generation for Offline Reinforcement Learning
Xiamen University（厦门大学）；Hong Kong University of Science and Technology（香港科技大学）；National University of Defense Technology（国防科技大学）；Jimei University（集美大学）
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes
Northwestern University（西北大学）；George Washington University（乔治华盛顿大学）；Yale University（耶鲁大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Contextual Transformer for Offline Meta Reinforcement Learning
University of Chinese Academy of Sciences（中国科学院大学）；Peking University（北京大学）；University College London（伦敦大学学院）；Chinese Academy of Sciences（中国科学院）；King’s College London（伦敦国王学院）
Vector-Valued Distributional Reinforcement Learning Policy Evaluation: A Hilbert Space Embedding Approach
Department of Statistics, University of Illinois Urbana-Champaign（统计系，伊利诺伊大学厄巴纳-香槟分校）；Department of Bioinformatics and Biostatistics, University of Louisville（生物信息学与生物统计学系，路易斯维尔大学）
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）
Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning
VTT Technical Research Centre of Finland（芬兰VTT技术研究中心）；Zhejiang Lab（之江实验室）；Zhejiang University（浙江大学）；Auburn University（奥本大学）；University of Electro-Communications（电气通信大学）；University of Oulu（奥卢大学）
Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Alibaba Group（阿里巴巴集团）
Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
Taobao \& Tmall Group of Alibaba City University of Hong Kong Hangzhou China；Southern University of Science；Technology, City University of Hong Kong Hong Kong China；City University of Hong Kong Hong Kong China；University of Queensland Brisbane Australia；Taobao \& Tmall Group of Alibaba Beijing China；Taobao \& Tmall Group of Alibaba City University of Hong Kong；Technology, City University of Hong Kong；City University of Hong Kong；University of Queensland；Taobao \& Tmall Group of Alibaba
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models
School of Computer Science, McGill University（麦吉尔大学计算机科学学院）
