The COVID-19 pandemic underscored a critical need for intervention strategies that balance disease containment with socioeconomic stability. We approach this challenge by designing a framework for modeling and evaluating disease-spread prevention strategies. Our framework leverages multi-objective reinforcement learning (MORL) - a formulation necessitated by competing objectives - combined with a new stochastic differential equation (SDE) pandemic simulator, calibrated and validated against global COVID-19 data. Our simulator reproduces national-scale pandemic dynamics with orders of magnitude higher fidelity than other models commonly used in reinforcement learning (RL) approaches to pandemic intervention. Training a Pareto-Conditioned Network (PCN) agent on this simulator, we illustrate the direct policy trade-offs between epidemiological control and economic stability for COVID-19. Furthermore, we demonstrate the framework's generality by extending it to pathogens with different epidemiological profiles, such as polio and influenza, and show how these profiles lead the agent to discover fundamentally different intervention policies. To ground our work in contemporary policymaking challenges, we apply the model to measles outbreaks, quantifying how a modest 5% drop in vaccination coverage necessitates significantly more stringent and costly interventions to curb disease spread. This work provides a robust and adaptable framework to support transparent, evidence-based policymaking for mitigating public health crises.
C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front
University of Oxford（牛津大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Queen Mary University of London（伦敦大学玛丽女王学院）；Microsoft Research Asia（微软亚洲研究院）；Peking University（北京大学）；University of Alberta（阿尔伯塔大学）
Limitations of Scalarisation in MORL: A Comparative Study in Discrete Environments
School of Mathematics, Statistics and Computer Science（数学、统计与计算机科学学院）；University of KwaZulu-Natal（科萨努卢纳大学）；Centre for Artificial Intelligence Research (CAIR)（人工智能研究中心）
Interpretability by Design for Efficient Multi-Objective Reinforcement Learning
School of Informatics, The University of Edinburgh（信息学院，爱丁堡大学）；School of Mathematics, The University of Edinburgh（数学学院，爱丁堡大学）；Edinburgh Centre for Robotics（爱丁堡机器人中心）
gTLO: A Generalized and Non-linear Multi-Objective Deep Reinforcement Learning Approach
Institute for Applied Materials - Computational Materials Science (IAM - CMS), Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院应用材料研究所——计算材料科学方向（IAM-CMS））；Intelligent Systems Research Group (ISRG), Karlsruhe University of Applied Sciences（卡尔斯鲁厄应用技术大学智能系统研究组（ISRG））
Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning
University of Rome ‘Tor Vergata’（罗马第二大学）；INFN（意大利国家核物理研究所）；École Normale Supérieure（巴黎高等师范学院）；The Abdus Salam International Centre for Theoretical Physics（阿卜杜勒·萨拉姆国际理论物理中心）；Istituto dei Sistemi Complessi, CNR（意大利国家研究委员会复杂系统研究所）
Center for Intelligent and Networked System (CFINS)（智能与网络化系统中心）；Department of Automation（自动化系）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Beijing Key Laboratory of Embodied Intelligence Systems（北京具身智能系统重点实验室）；Tsinghua University（清华大学）
Department of Electrical Engineering and Computer Science, Pennsylvania State University, PA, USA（电气工程与计算机科学系，宾夕法尼亚州立大学）；College of Information Sciences and Technology, Pennsylvania State University, PA, USA（信息科学与技术学院，宾夕法尼亚州立大学）
Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control
Centre for Intelligent and Secure Industrial Automation（智能与安全工业自动化中心）；Salzburg University of Applied Sciences（萨尔茨堡应用科学大学）；Paris Lodron University of Salzburg（萨尔茨堡巴黎洛登大学）；Kempten University of Applied Sciences（凯普滕应用科学大学）
提出多目标强化学习框架，通过复合奖励平衡 Quanser Aero 2 的俯仰跟踪与能耗，并分析能量惩罚权重及 Adam 优化器对 Pareto 最优性的影响。
