Reinforcement Learning (RL) is increasingly applied to large-scale decision-making problems like logistics, scheduling, and recommender systems, but existing algorithms struggle with the curse of dimensionality in such large discrete action spaces. We propose Distance-Guided Reinforcement Learning (DGRL), combining Sampled Dynamic Neighborhoods and Distance-Based Updates to enable efficient RL in problems with up to $10^{20}$ actions. Unlike prior methods, DGRL performs stochastic volumetric exploration and transforms policy optimization into a stable regression task, decoupling gradient variance from action space cardinality. On structured tasks, DGRL provably guarantees local value improvement. DGRL naturally generalizes to hybrid continuous-discrete action spaces. We demonstrate performance improvements of up to 66% against state-of-the-art benchmarks across regularly and irregularly structured environments, while simultaneously improving convergence speed and computational complexity.
Optimal trajectory-guided stochastic co-optimization for e-fuel system design and real-time operation
Department of Chemical Engineering（化学工程系）；Massachusetts Institute of Technology（麻省理工学院）；Department of Chemical and Biomolecular Engineering（化学与生物分子工程系）；Yonsei University（延世大学）；Department of Chemical Engineering and Materials Science（化学工程与材料科学系）；Ewha Womans University（成均馆大学）；Institute for Multiscale Matter and Systems (IMMS)（多尺度物质与系统研究所）
Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening
Scientific Computing and Imaging Institute, School of Computing, University of Utah（犹他大学计算学院科学计算与成像研究所）；Department Of Biochemistry, University of Utah（犹他大学生物化学系）
Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather
1Dept. of Computer Science, Midwestern State University, Dallas, TX, USA 2O\&M Analytics, AES Clean Energy, The AES Corporation, Louisville, CO, USA 3Dept. of Information；Communication Engineering, Pabna University of Science
LLM-guided Chemical Process Optimization with a Multi-Agent Approach
Department of Chemical Engineering, Carnegie Mellon University（化学工程系，卡内基梅隆大学）；Department of Mechanical Engineering, Carnegie Mellon University（机械工程系，卡内基梅隆大学）；Department of Biomedical Engineering, Carnegie Mellon University（生物医学工程系，卡内基梅隆大学）；Machine Learning Department, Carnegie Mellon University（机器学习系，卡内基梅隆大学）；Department of Chemical and Biomolecular Engineering, University of Nebraska--Lincoln（化学与生物分子工程系，内布拉斯加大学林肯分校）
