Multi-Objective Reinforcement Learning

Also known as MORL

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11 papers in the last four weeks, up 175% on the four weeks before. 0.1% of all new papers.

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Strategic Investment Decision Making for Value Creation in Energy Transition: A Reinforcement Learning Approach
Oct 7, 2026Yasaman Cheraghi, Reidar B. Bratvold, Aojie Hong +2Sequential Decision Making Decision-Making under Uncertainty
Department of Energy and Petroleum Engineering, University of Stavanger, Norway · Independent Researcher, Stavanger, Norway · NORCE Norwegian Research Centre, Bergen, Norway
Transporting Unsecured Stacked Payloads with a Quadrupedal Robot via Multi-Objective Reinforcement Learning
Oct 5, 2026Nobuo Namura, Masayuki Hiromoto, Kento Uemura +2Multi-Objective Reinforcement Learning Robot Policy Adaptation
Fujitsu Limited, Kawasaki, Japan
PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots
Oct 1, 2026Amr Mousa, Rifny Rachman, Neil Karavis +2Multi-Objective Reinforcement Learning Energy-Efficient Robot Locomotion
The University of Manchester, United Kingdom · BAE Systems, United Kingdom · University of Warwick, United Kingdom
Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL
Sep 30, 2026Tong Zheng, Skylar Zhai, Zhan Cheng +7Multi-Objective Reinforcement Learning Reinforcement Learning
University of Chinese Academy of Sciences · University of Minnesota Twin Cities · University of Wisconsin–Madison +5
MoFlow: Multi-Objective Agentic Workflow Generation
Sep 29, 2026Yining Lu, Aurelie Lozano, Xi Yang +3Multi-Objective Reinforcement Learning Agentic Workflows
University of Notre Dame · IBM
Adaptive Reward Routing: Dynamic Multi-Reward Optimization for Joint Audio-Video Diffusion via Forward-Process RL
Sep 29, 2026Songlin Yang, Xiaotong Zhao, Jiacheng Zhang +5Multi-Objective Reinforcement Learning Cross-Modal Alignment
Tencent Video · The University of Hong Kong
CorrGRPO: Correlation-Normalized GRPO for Multi-Reward Learning
Sep 29, 2026Wenbin Hu, Huihao Jing, Haochen Shi +3Multi-Objective Reinforcement Learning RL for Language Models
Hong Kong University of Science and Technology
ORPG: Reconciling Multiple Reward Objectives through Objective-wise Policy Gradients
Sep 28, 2026Shicheng Fang, Yiwen Zhao, Wenbo Tian +4Multi-Objective Reinforcement Learning Gradient Interference
Fudan University · Shanghai Innovation Institute
AlphaPareto: Formulaic Alpha Discovery with LLM-Guided Multi-Objective Reinforcement Learning
Sep 28, 2026Yingbo Zhao, Zeyu Yang, Zhoufan ZhuMulti-Objective Reinforcement Learning Non-Stationary RL
School of Economics Xiamen University Fujian, China · Paula and Gregory Chow Institute for Studies in Economics Xiamen University Fujian, China · School of Economics & Wang Yanan Institute for Studies in Economics Xiamen University Fujian, China
Evolution of fairness in multi-objective reinforcement learning framework
Sep 28, 2026Jingyi Zhang, Xin Ou, Guozhong Zheng +3Multi-Objective Reinforcement Learning Game Theory
School of Physics and Information Technology, Shaanxi Normal University, Xi’an 710061, P. R. China · School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China · School of Physics, Ningxia University, Yinchuan 750021, P. R. China
On Preference Coverage Collapse from Hindsight Relabeling in Multi-Objective Reinforcement Learning
Sep 22, 2026Baptiste Bonin, Caro Strickland, Audrey DurandMulti-Objective Reinforcement Learning Experience Replay
Mila (Quebec AI Institute) · Université Laval · CIFAR AI Chair
CellRFT: Reinforcement Fine-Tuning for Single-Cell Perturbation Modeling
Sep 17, 2026Jie Yan, Li Liu, Hanze Guo +7Multi-Objective Reinforcement Learning Single-Cell Perturbation Modeling
Chinese Academy of Sciences · Peking University · Renmin University of China +2
A Better Spur Should Start From Each Objective
Sep 8, 2026Shanwen Mao, Hao Zhang, Guangtao nie +4Multi-Objective Reinforcement Learning Gradient Interference
Harbin Institute of Technology, Harbin, China · JD.com Inc., Beijing, China
Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization
Sep 2, 2026Giovanni Dispoto, Marcello Restelli, Carmine VentrePortfolio Optimization Finance
Politecnico di Milano · King’s College London
Uncovering and Mitigating Aggregation-Induced Reward Hacking in Multi-Reward Reinforcement Learning
Aug 31, 2026Yu Yuan, Yaoyou Fan, Lili Zhao +5Multi-Objective Reinforcement Learning Reward Hacking
University of Science and Technology of China · 2State Key Laboratory of Cognitive Intelligence · 3Peking University +1
It's a matter of timescale: non-linear utility in successor features and multi-objective planning and learning
Aug 26, 2026Liam P. H. Mertens, Lucas N. Alegre, Florent Delgrange +3Multi-Objective Reinforcement Learning Sequential Decision Making
AI Lab, Vrije Universiteit Brussel, Belgium · Institute of Informatics - Federal University of Rio Grande do Sul · Federation University Australia
Learn What's Left, Not What's Mastered: Saturation Aware Advantage Reweighting for Multi-Reward Policy Optimization
Aug 17, 2026Yixuan Wang, Yifei Chen, Haichao Zhang +6Multi-Objective Reinforcement Learning RL for Language Model Reasoning
University of Florida · UC San Diego · Northeastern University +3
Momba: Network Modernization Improves Multi-Objective Reinforcement Learning
Aug 7, 2026Adam Štafa, Santeri Heiskanen, Petr Novotný +1Multi-Objective Reinforcement Learning
Faculty of Informatics, Masaryk University · Department of Electrical Engineering and Automation, Aalto University
ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration
Aug 5, 2026Osei Brempong, Mohammed Ayman Habib, Vivan Poddar +1Multi-Objective Reinforcement Learning Electronic Design Automation
University of Utah, Salt Lake City, UT, USA
SMOPD: Multi-Reward Reinforcement Learning via Specialize-and-Merge Online Policy Distillation
Aug 4, 2026Wen Wang, Jiahua Bao, Tu Yongsiqi +8Multi-Objective Reinforcement Learning
Qwen Large Model Application Team, Alibaba · University of Chinese Academy of Sciences
A Constitution-Grid Instrument for Data-Efficient RL Alignment (C-Guard)
Jul 31, 2026Xianling ZhangMulti-Objective Reinforcement Learning LLM Safety Alignment
Independent Researcher
LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback
Jul 31, 2026Manith Adikari, Bei Peng, Samuele Vinanzi +1Multi-Objective Reinforcement Learning Reinforcement Learning
Department of Computer Science, University of Manchester · Centre for Robotics & AI, University of Manchester · School of Computer Science, University of Sheffield +1
Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL
Jul 31, 2026Ruiming Liang, Yi Zhong, Yizhen Yuan +6Multi-Objective Reinforcement Learning RL for Language Models
1Fundation Model Research Center, CASIA · School of Artificial Intelligence, UCAS · Institute for AI Industry Research (AIR), Tsinghua University +1
Semantic Pareto-DQN: A Multi-Objective Reinforcement Learning Framework for Financial Anomaly Detection
Jul 10, 2026Cláudio Lúcio do Val Lopes, Lucca Machado da SilvaMulti-Objective Reinforcement Learning Class-Imbalanced Learning
A3Data
Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms
Jul 7, 2026Marcos Eduardo Cruz Victorio, Karl MasonMulti-Objective Reinforcement Learning Differential Evolution
University of Galway, Ireland
Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization
Jul 7, 2026Sounaq Das, Tanmay Sen, Raghu Nandan Sengupta +1Multi-Objective Reinforcement Learning Risk-Sensitive RL
Indian Institute of Management Amritsar · Amritsar, India · SQC & OR Unit +7
Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimization
Jul 3, 2026Georg Schäfer, Jakob Rehrl, Stefan Huber +1RL Control Multi-Objective Reinforcement Learning
Josef Ressel Centre for Intelligent and Secure Industrial Automation, Salzburg University of Applied Sciences, Salzburg, Austria · Department of Artificial Intelligence and Human Interfaces, Paris Lodron University of Salzburg, Salzburg, Austria
AETDICE: Unified Framework and Offline Optimization for Nonlinear Multi-Objective RL
Jun 30, 2026Woosung Kim, Youngjun Suh, Jinho Lee +2Multi-Objective Reinforcement Learning Offline RL
1Korea University · 2Yonsei University · 3Gauss Labs Inc.
A Three-Phase Foundation Model for Tax-Aware Personalized Portfolio Management
Jun 30, 2026Ramin PishehvarMulti-Objective Reinforcement Learning Portfolio Optimization
Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning
Jun 29, 2026Antonio Marino, Esteban Restrepo, Soon-jo Chung +2Multi-Objective Reinforcement Learning Multi-Robot Coordination
University of Cambridge · CNRS, Univ Rennes, Inria, IRISA · Division of Engineering and Applied Science, California Institute of Technology

Oct 7, 2026, cs.CEJ/K move · Enter open · S save

Strategic Investment Decision Making for Value Creation in Energy Transition: A Reinforcement Learning Approach

Yasaman Cheraghi, Reidar B. Bratvold, Aojie Hong, Ressi B. Muhammad+1

Department of Energy and Petroleum Engineering, University of Stavanger, Norway · Independent Researcher, Stavanger, Norway · NORCE Norwegian Research Centre, Bergen, Norway

Sequential Decision Making Decision-Making under Uncertainty Multi-Objective Reinforcement Learning

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The global challenge of climate change has driven significant steps to reduce CO2 emissions, guided by international agreements like the Paris Agreement of 2015. Acting too slowly could result in future losses and reputational damage, while moving too quickly could jeopardize shareholder value due to the marginal profitability or potential losses due to technology immaturity of many renewable projects. To navigate this complex transition, energy companies must adopt Sequential Decision Making (SDM) strategies to maximize value creation from decision flexibility under uncertainties. To support this, we developed a custom simulation environment to model the dynamic energy landscape up to 2050. Building on this, we designed a multi-criteria SDM framework that explores various decision strategies related to different portfolios for allocating funds across three sectors: oil & gas, renewables, and CO2 reduction. It aims to maximize value during the transition while accounting for uncertainties in productions, energy prices, and costs. This framework has three objectives: maximizing profit, minimizing CO2 social costs, and enhancing competitive advantage in the renewable energy sector. This research evaluates the use of Reinforcement Learning (RL) to identify optimal investment policies within the defined SDM framework. The agent's sequential decisions shape a virtual dynamic environment by influencing key variables such as oil and gas production, renewable energy output, CO2 emissions, and revenues. Through repeated interaction, the RL algorithm explores the state space and learns an optimal policy under uncertainty. We benchmark the RL strategy against a set of manually defined baseline policies and find it consistently outperforms them in adaptability and long-term value creation.

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