Multi-Objective Reinforcement Learning

Also known as MORL

Overview

Multi-objective reinforcement learning studies sequential decisions where goals such as safety, efficiency, fairness, and reward quality can conflict. Common approaches learn a set of Pareto-optimal policies or condition one policy on user preferences, using methods such as scalarization, vector-valued critics, policy-gradient updates, and multi-agent coordination. Researchers apply these methods in robotics, traffic and energy control, recommendation, and language-model alignment, evaluating trade-offs with Pareto-front coverage, hypervolume, and task-specific benchmarks. A central challenge is balancing reward signals without letting easier objectives dominate or causing reward hacking, while ensuring policies reliably respond to changing preferences.

Latest papers

CardsList

146 papers25 papers in the last four weeks, up 150% on the four weeks before. 0.1% of all new papers.

  1. PCHC: Enabling Preference Conditioned Humanoid Control via Multi-Objective Reinforcement Learning

    Mar 25, 2026Huanyu Li, Dewei Wang, Xinmiao Wang +4Robotic ControlHumanoid Robot Control

  2. Learning What Matters Now: Dynamic Preference Inference under Contextual Shifts

    Mar 24, 2026Xianwei Cao, Dou Quan, Zhenliang Zhang +1Preference LearningMulti-Objective Reinforcement Learning

  3. TREX: Trajectory Explanations for Multi-Objective Reinforcement Learning

    Mar 23, 2026Dilina Rajapakse, Juan C. Rosero, Ivana DusparicInterpretability in RLMulti-Objective Reinforcement Learning

  4. MO-Playground: Massively Parallelized Multi-Objective Reinforcement Learning for Robotics

    Mar 10, 2026Neil Janwani, Ellen Novoseller, Vernon J. Lawhern +1GPU-Accelerated RLMulti-Objective Reinforcement Learning

  5. Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing

    Mar 8, 2026Nikita Sarawgi, Omey M. Manyar, Fan Wang +3RoboticsMulti-Objective Reinforcement Learning

  6. Adaptive Double-Booking Strategy for Outpatient Scheduling Using Multi-Objective Reinforcement Learning

    Mar 7, 2026Ninda Nurseha Amalina, Heungjo AnHealthcareMulti-Objective Reinforcement Learning

  7. Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Driving

    Mar 6, 2026Ahmed Abouelazm, Jonas Michel, Daniel Bogdoll +2Multi-Objective Reinforcement LearningRL for Autonomous Driving

  8. MIRACL: A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation

    Mar 5, 2026Rifny Rachman, Josh Tingey, Richard Allmendinger +3Supply Chain OptimizationMulti-Objective Reinforcement Learning

  9. Decoupling Task and Behavior: A Two-Stage Reward Curriculum in Reinforcement Learning for Robotics

    Mar 5, 2026Kilian Freitag, Knut Åkesson, Morteza Haghir ChehreghaniRL for RoboticsReinforcement Learning

  10. Reward-Conditioned Reinforcement Learning

    Mar 5, 2026Michal Nauman, Marek Cygan, Pieter AbbeelReinforcement LearningOff-Policy RL

  11. Alternating Reinforcement Learning with Contextual Rubric Rewards: Beyond the Scalarization Strategy

    Mar 4, 2026Guangchen Lan, Lian Xiong, Xin Zhou +7Reinforcement LearningRubric-Based RL

  12. [Re] FairDICE: A Fair Tradeoff in Multi-objective Offline RL

    Mar 3, 2026Peter Adema, Karim Galliamov, Aleksey Evstratovskiy +1Offline RLMulti-Objective Reinforcement Learning

  13. Post Hoc Extraction of Pareto Fronts for Continuous Control

    Mar 3, 2026Raghav Thakar, Gaurav Dixit, Kagan TumerMulti-Objective Reinforcement LearningPareto Optimization

  14. MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning

    Feb 28, 2026Tianmeng Hu, Biao Luo, Chunhua Yang +1Multi-Agent Reinforcement LearningMulti-Objective Reinforcement Learning

  15. Multi-Objective Reinforcement Learning for Large-Scale Tote Allocation in Human-Robot Collaborative Fulfillment Centers

    Feb 27, 2026Sikata Sengupta, Guangyi Liu, Omer Gottesman +4Multi-Objective Reinforcement LearningSupply Chain Management

  16. PRISM: Parallel Reward Integration with Symmetry for MORL

    Feb 20, 2026Finn van der Knaap, Kejiang Qian, Zheng Xu +1Equivariant Policy LearningReinforcement Learning

  17. Flow Matching for Offline Reinforcement Learning with Discrete Actions

    Feb 5, 2026Fairoz Nower Khan, Nabuat Zaman Nahim, Ruiquan Huang +2Markov ChainsReinforcement Learning

  18. Spatiotemporal Continual Federated Learning for Agentic Multi-UAV Edge Networks: Mitigating Catastrophic Forgetting

    Jan 29, 2026Chuan-Chi LaiMulti-Objective Reinforcement LearningFederated RL

  19. GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning

    Jan 28, 2026Zhiheng Jiang, Yunzhe Wang, Ryan Marr +3Multi-Objective Reinforcement LearningRL Benchmarks

  20. Preference-Conditioned Multi-Objective Reinforcement Learning for Tactical Decision Making in Heavy-Duty Vehicles

    Jan 26, 2026Deepthi Pathare, Leo Laine, Morteza Haghir ChehreghaniMulti-Objective Reinforcement LearningRL for Autonomous Driving

  21. Game-Theoretic Understandings of Multi-Agent Systems with Multiple Objectives

    Sep 27, 2025Zixin Lin, Qianyi Tan, Sihong He +1Game TheoryMulti-Objective Reinforcement Learning

  22. 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 LearningCollision Avoidance