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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Micro-Swarm Locomotion Optimization in Dynamic Flow using Multi-Objective Multi-Agent Reinforcement Learning
May 24, 2026Josef Berman, Oren GalMulti-Objective Reinforcement Learning Multi-Agent System Optimization
The Hatter Department of Marine Technologies, Leon H. Charney School of Marine Sciences, University of Haifa, Israel.
Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration
May 23, 2026Ninglin Ou, Mohammad A. Razzaque, Iftekher Islam Shovon +5RL Control Multi-Objective Reinforcement Learning
anasc Research, School of Computer Science & IT, University College Cork, IE · School of Computing, Engineering and Digital Technologies, Teesside University, UK · International Energy Research Centre, Tyndall National Institute, Cork, IE +1
Vector Policy Optimization: Training for Diversity Improves Test-Time Search
May 21, 2026Ryan Bahlous-Boldi, Isha Puri, Idan Shenfeld +6Multi-Objective Reinforcement Learning Reinforcement Learning
1MIT · 2Improbable AI Lab · 4Sakana AI +1
Two is better than one: A Collapse-free Multi-Reward RLIF Training Framework
May 21, 2026Shourov Joarder, Diganta Sikdar, Ahsan Habib Akash +2Multi-Objective Reinforcement Learning Intrinsic Reward Methods
Bangladesh University of Engineering and Technology, Bangladesh · West Virginia University, USA · University of Aberdeen, UK +2
Multi-Objective and Mixed-Reward Reinforcement Learning via Reward-Decorrelated Policy Optimization
May 13, 2026Yang Bai, Kaiyuan Liu, Ziyuan Zhuang +5Multi-Objective Reinforcement Learning RL for Language Models
Meituan, China
Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization
May 12, 2026Alejandro Murillo-Gonzalez, Mahmoud Ali, Lantao LiuMulti-Objective Reinforcement Learning Policy Optimization
Indiana University–Bloomington
Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation
May 12, 2026Ziyad Sheebaelhamd, Luca Viano, Volkan Cevher +1Multi-Objective Reinforcement Learning Imitation Learning
University of Tübingen · EPFL · University of Technology Nuremberg
Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity
May 12, 2026Zijiang Yan, Hao Zhou, Wael Jaafar +4Multi-Objective Reinforcement Learning Language Model-Based Control
Controllability in preference-conditioned multi-objective reinforcement learning
May 11, 2026Pau de las Heras Molins, Beyazit Yalcinkaya, Lasse Peters +2Multi-Objective Reinforcement Learning Preference-Based RL
LTCI, Télécom Paris, Institut Polytechnique de Paris · University of California, Berkeley · TU Delft +1
A Single Deep Preference-Conditioned Policy for Learning Pareto Coverage Sets
May 9, 2026Akihiro Kubo, Kosuke Nakanishi, Shin IshiiMulti-Objective Reinforcement Learning Policy Optimization
International Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan · ATR Neural Information Analysis Laboratories, Kyoto, Japan · Department of Information Science, Kyoto University, Kyoto, Japan
BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning
May 8, 2026Shaokai Ye, Vasileios Saveris, Yihao Qian +3Multi-Objective Reinforcement Learning Image Captioning
Apple
A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems
May 8, 2026Yichu Zhou, Mehdi Ben Ayed, Lin Yang +9Multi-Objective Reinforcement Learning Recommender Systems
Pinterest Inc. Seattle, Washington, USA · Pinterest Inc. New York City, New York, USA · Pinterest Inc. San Francisco, California, USA +1
MARBLE: Multi-Aspect Reward Balance for Diffusion RL
May 7, 2026Canyu Zhao, Hao Chen, Yunze Tong +3Multi-Objective Reinforcement Learning Diffusion Model Alignment
Zhejiang University · Work done during an internship at HiThink. · HiThink +1
Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks
May 4, 2026Po-Heng Chou, Chiapin Wang, Chung-Chi Huang +1Multi-Objective Reinforcement Learning Deep Q-Learning
Research Center for Information Technology Innovation (CITI), Academia Sinica (AS), Taipei 11529, Taiwan · Department of Electrical Engineering, National Taiwan Normal University (NTNU), Taipei 106308 Taiwan
Protein-Conditioned Multi-Objective Reinforcement Learning for Full-Length mRNA Design
May 2, 2026Zixi Shao, Tao Wang, Yibei Xiao +1Multi-Objective Reinforcement Learning Molecular Generation
Nanjing Foreign Language School, Nanjing, China · School of Science, China Pharmaceutical University, Nanjing, China · School of Pharmacy, China Pharmaceutical University, Nanjing, China
A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning
Apr 27, 2026Ying-Tu Chen, Wei Hung, Bing-Shu Wu +2Multi-Objective Reinforcement Learning Reinforcement Learning
National Yang Ming Chiao Tung University, Hsinchu, Taiwan · Massachusetts Institute of Technology
POCA: Pareto-Optimal Curriculum Alignment for Visual Text Generation
Apr 27, 2026Yaohou Fan, Qingzhong Wang, Yongsong Huang +3Multi-Objective Reinforcement Learning RL for Image Generation
Tohoku University · Amazon Web Services
ParetoSlider: Diffusion Models Post-Training for Continuous Reward Control
Apr 22, 2026Shelly Golan, Michael Finkelson, Ariel Bereslavsky +2Multi-Objective Reinforcement Learning RL for Image Generation
Tel Aviv University · Lightricks · Adobe Research
Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation
Apr 21, 2026Rui Li, Ke Hao, Yuanzhi Liang +4Multi-Objective Reinforcement Learning Video Diffusion Models
University of Science and Technology of China HeFei, China · Shanghai Jiao Tong University ShangHai, China · TeleAI ShangHai, China
Multi-objective Reinforcement Learning With Augmented States Requires Rewards After Deployment
Apr 17, 2026Peter Vamplew, Cameron FoaleMulti-Objective Reinforcement Learning Reinforcement Learning
Institute of Innovation, Science and Sustainability, Federation University Australia, University Drive, Mt Helen, 3350, Victoria, Australia
Spatiotemporal Continual Federated Learning for Agentic Multi-UAV Edge Networks: Mitigating Catastrophic Forgetting
Jan 29, 2026Chuan-Chi LaiMulti-Objective Reinforcement Learning Federated RL
Department of Communications Engineering, National Chung Cheng University, Minxiong Township, Chiayi County 621301, Taiwan · Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI), National Chung Cheng University, Minxiong Township, Chiayi County 621301, Taiwan
GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning
Jan 28, 2026Zhiheng Jiang, Yunzhe Wang, Ryan Marr +3Multi-Objective Reinforcement Learning RL Benchmarks
University of California, Los Angeles · USC Institute for Creative Technologies · U.S. Army DEVCOM Army Research Laboratory
Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework
Aug 22, 2025Surya Murthy, Zhenyu Gao, John-Paul Clarke +1Multi-Objective Reinforcement Learning Collision Avoidance
Graduate Research Assistant, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin · Postdoctoral Fellow, Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin · Professor and Ernest Cockrell, Jr. Memorial Chair in Engineering, Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin +1

May 24, 2026, cs.ROJ/K move · Enter open · S save

Micro-Swarm Locomotion Optimization in Dynamic Flow using Multi-Objective Multi-Agent Reinforcement Learning

Josef Berman, Oren Gal

The Hatter Department of Marine Technologies, Leon H. Charney School of Marine Sciences, University of Haifa, Israel.

Multi-Objective Reinforcement Learning Multi-Agent System Optimization Swarm Robotics Multi-Robot Coordination Decentralized MARL Multi-Agent Reinforcement Learning

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Coordinating micro-robotic swarms in realistic, time-dependent fluid environments remains a major challenge for biomedical and environmental applications. We present a hybrid CFD-MO-MARL (Computational Fluid Dynamics-Multi Objective-Multi Agent Reinforcement Learning) framework that couples a high-fidelity incompressible Navier--Stokes solver with decentralized proximal policy optimization to learn swarm control policies in oscillatory flow. Sixteen magnetically actuated micro-robots were simulated to navigate a pulsatile arterial waveform within a 2 mm channel while jointly optimizing upstream progression, energy efficiency, and motion smoothness. Conflicting objectives are resolved using Projected Conflicting Gradient (PCGrad) surgery. Without PCGrad, energy and smoothness rewards collapse during training, demonstrating that gradient conflict resolution is essential for stable multi-objective learning. The converged policy achieves progress rewards of 6.5-7.0, energy efficiency of 0.63-0.65, and smoothness of 0.97-0.99, outperforming brute-force baselines by more than 8 reward units on the primary objective. Training reveals three emergent behaviors not encoded in the reward function: hydrodynamic throttling formations that reduce peak flow velocities, a cycle-synchronized ratchet mechanism that exploits flow reversals for upstream movement, and individualized final-approach strategies near the target boundary. These results demonstrate that physically realistic fluid--agent interactions can be integrated directly into multi-objective reinforcement learning, providing a scalable framework for micro-swarm control in biomedical navigation, environmental monitoring, and microfluidic systems.

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