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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A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions
Jun 27, 2026Junhan Li, Yuxin Zhang, Haoran Wang +1Multi-Objective Reinforcement Learning Game Theory
Department of Computer Science Nanjing University
Deterministic Pareto-Optimal Policy Synthesis for Multi-Objective Reinforcement Learning
Jun 24, 2026Aniruddha Joshi, Niklas Lauffer, Sanjit SeshiaMulti-Objective Reinforcement Learning Multi-Objective Optimization
University of California Berkeley, USA.
MAPL: Multi-Objective Preference Learning for Robot Locomotion
Jun 24, 2026Xiyue Chen, Muhan Lin, Shuyang Shi +1Multi-Objective Reinforcement Learning Reward Modeling
Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47906, USA · Department of Computer Science, Purdue University, West Lafayette, IN 47906, USA · independent researcher
Bias-Controlled Primal-Dual Natural Actor-Critic: Optimal Rates for Constrained Multi-Objective Average-Reward RL
Jun 23, 2026Ankur Naskar, Swetha Ganesh, Vaneet AggarwalMulti-Objective Reinforcement Learning Reinforcement Learning
Indian Institute of Science, Bengaluru, India · Purdue University, USA
Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation
Jun 23, 2026Cláudio Lúcio Do Val Lopes, Lucca Machado da Silva, André de Oliveira BrandãoMulti-Objective Reinforcement Learning Algorithmic Fairness
A3Data - MG, Brazil
MORL-A2C: Multi-Objective Reinforcement Learning Reranker for Optimizing Healthiness in MOPI-HFRS
Jun 22, 2026Aarya Vasantlal, Joshua Zolla, Chuxu ZhangMulti-Objective Reinforcement Learning Sequential Decision Making
University of Connecticut, Storrs, CT, USA
Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation
Jun 20, 2026Rifny Rachman, Bahrul Ilmi Nasution, Josh Tingey +3Multi-Objective Reinforcement Learning Multi-Objective Evolutionary Optimization
The University of Manchester, United Kingdom · Peak AI, Ltd, United Kingdom
Post-Training Speech Enhancement Language Models with Perceptual Rewards
Jun 19, 2026Frédéric Berdoz, Luca A. Lanzendörfer, Antonis Asonitis +1Multi-Objective Reinforcement Learning RL for Language Models
ETH Zürich, Switzerland
Objective-Behavior Alignment: Diagnostics for MORL Policy Selection
Jun 19, 2026Antonio Mone, Zuzanna Osika, Florian Felten +4Multi-Objective Reinforcement Learning Reinforcement Learning
Delft University of Technology, the Netherlands · ETH Zurich, Switzerland & herve.review, Belgium · ETH Zurich, Switzerland
A Multi-Agent system for Multi-Objective constrained optimization
Jun 18, 2026Federica FilippiniMulti-Objective Reinforcement Learning Multi-Agent System Optimization
University of Milano-Bicocca Milan, Italy
Pareto Q-Learning with Reward Machines
Jun 17, 2026Arnaud Lequen, Clément Legrand-Lixon, Léo SaulièresMulti-Objective Reinforcement Learning Reinforcement Learning
Linköping University, Sweden · Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStal, F-59000 Lille, France · Univ. Toulouse, INRAE-MIAT, Toulouse, France
Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning
Jun 16, 2026Umer Siddique, Peilang Li, Yongcan CaoMulti-Objective Reinforcement Learning Algorithmic Fairness
Department of Electrical and Computer Engineering, University of Texas at San Antonio
GD $^2$ PO: Mitigating Multi-Reward Conflicts via Group-Dynamic reward-Decoupled Policy Optimization
Jun 15, 2026Haotian Liu, Yihao Liu, Jingwei Ni +11Multi-Objective Reinforcement Learning RL for Language Models
Alibaba · Renmin University of China · Peking University +3
Towards Pareto-Optimal Tool-Integrated Agents with Pareto Ranking Policy Optimization
Jun 15, 2026Junyi Li, Xiaowei Qian, Yingyi Zhang +6Multi-Objective Reinforcement Learning Multi-Objective Language Model Alignment
Department of Data Science, City University of Hong Kong, Hong Kong, China · Zhejiang University of Technology, Hangzhou, China · Huawei Technologies Noah’s Ark Lab, Hong Kong, China
Learning Coordinated Preference for Multi-Objective Multi-Agent Reinforcement Learning
Jun 12, 2026Pengxin Wang, Lihao Guo, Yi Xie +3Multi-Objective Reinforcement Learning Multi-Agent System Optimization
Department of Electrical and Computer Engineering, University of Arizona
Multi-Agent Reinforcement Learning from Delayed Marketplace Feedback for Objective-Weight Adaptation in Three-Sided Dispatch
Jun 11, 2026Haochen Wu, Yi Hou, Shiguang XieMulti-Objective Reinforcement Learning Reinforcement Learning
DoorDash Inc.
Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation
Jun 10, 2026Mehmet Turan YardımcıMulti-Objective Reinforcement Learning Humanoid Loco-Manipulation
Department of Computer Engineering C¸ukurova University Adana, Türkiye
ReCal: Reward Calibration for RL-based LLM Routing
Jun 10, 2026Qihang Yu, Hanwen Tong, Zhengqi Zhang +5Multi-Objective Reinforcement Learning RL for Language Models
Zhejiang University · Ant Group · Shanghai AI Laboratory
Event-Driven Reinforcement Learning Enables Long-Horizon Control in Semiconductor Fabrication
Jun 9, 2026Yavar Yeganeh, Mahsa Shekari, Nicla Frigerio +2RL Control Multi-Objective Reinforcement Learning
Politecnico di Milano · STMicroelectronics
One Model, Multiple Goals: Adaptive Multi-Objective Learning for E-commerce Dialogue Systems
Jun 8, 2026Mingzhe Li, Jing Xiang, Enguo Zhou +5Multi-Objective Reinforcement Learning RL for Language Models
SAW: Stage-Aware Dynamic Weighting for Multi-Objective Reinforcement Learning in Large Language Models
Jun 5, 2026Yuchen He, Baolong Bi, Shenghua Liu +7Multi-Objective Reinforcement Learning RL for Language Models
Institute of Computing Technology, Chinese Academy of Sciences · University of Electronic Science and Technology of China
COP-Q: Safety-First Reinforcement Learning for Robot Control via Cholesky-Ordered Projection
Jun 3, 2026Guopeng Li, Moritz A. Zanger, Matthijs T. J. Spaan +1Robotic Control Multi-Objective Reinforcement Learning
Department of Cognitive Robotics, Delft University of Technology · School of Transportation, Southeast University, China · Department of Intelligent Systems, Delft University of Technology, the Netherlands
Coordination Graphs for Constrained Multi-Agent Reinforcement Learning
Jun 1, 2026Santiago Amaya-Corredor, Miguel Calvo-Fullana, Anders JonssonMulti-Objective Reinforcement Learning Multi-Agent System Optimization
Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion
May 29, 2026Giseung Park, Hyunyoung Nam, Woohyeon Byeon +2Multi-Objective Reinforcement Learning Reinforcement Learning
Robotics Institute, University of Toronto, 55 St George St, Toronto, Canada. · School of Electical Engineering, KAIST, Daejeon 34141, Republic of Korea. · Faculty of Engineering, Bar-Ilan University, Ramat Gan 52900, Israel.
PEARL: Training Socratic Tutors with Pedagogically Aligned Reinforcement Learning
May 28, 2026Qikai Chang, Zhenrong Zhang, Linbo Chen +4Multi-Objective Reinforcement Learning Intelligent Tutoring Systems
University of Science and Technology of China · iFLYTEK Research
Extrapolative Weight Averaging Reveals Correctness-Efficiency Frontiers in Code RL
May 27, 2026Kunhao Zheng, Pierre Chambon, Juliette Decugis +4Multi-Objective Reinforcement Learning RL for Code Generation
Meta Superintelligence Labs - FAIR · Université Paris-Dauphine PSL
PIRS: Physics-Informed Reward Shaping for SAC-Based Building Energy Management
May 27, 2026Shadmehr Zaregarizi, Khashayar YavariMulti-Objective Reinforcement Learning Reward Shaping
Politecnico di Torino Turin, Italy
Focal Reward: Balanced Reinforcement Learning under Rubric-Based Rewards
May 26, 2026Yu Huang, Zihua Zhao, Zhaoxin Huan +9Multi-Objective Reinforcement Learning RL for Language Models
Shanghai Jiao Tong University · Ant Group
Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning
May 25, 2026Zhen Li, Jun Cai, Chao Yang +1Multi-Objective Reinforcement Learning Edge Inference
Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada · School of Automation, Guangdong University of Technology, China
DVAO: Dynamic Variance-adaptive Advantage Optimization for Multi-reward Reinforcement Learning
May 25, 2026Guochao Jiang, Jingyi Song, Guofeng Quan +3Multi-Objective Reinforcement Learning RL for Language Model Reasoning
Alibaba Cloud Computing

Jun 27, 2026, cs.CLJ/K move · Enter open · S save

A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions

Junhan Li, Yuxin Zhang, Haoran Wang, Minghao Chen

Department of Computer Science Nanjing University

Multi-Objective Reinforcement Learning Game Theory Automated Bidding Reward Design for RL

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Learning to bid in repeated multi-unit auctions with bandit feedback poses a fundamental challenge. Existing methods often rely on rigid explore-then-exploit schedules, assume stationary adversaries, and optimize solely for bidder utility, thereby limiting adaptability and strategic robustness. To address these limitations, we introduce the A3M framework, which integrates adaptive deep reinforcement learning (DRL), explicit adversarial reasoning, and principled multi-objective reward design for online auction strategy optimization. A3M employs an actor-critic DRL backbone to dynamically balance exploration and exploitation, an opponent model for fictitious play against non-stationary adversaries, and a composite reward function to jointly maximize utility, auctioneer revenue, and fairness. We provide the first comprehensive empirical evaluation of this integrated approach against established baselines in both discriminatory and uniform price auctions. Results show that A3M reduces final regret by 30--40% in standard settings, maintains robust performance against adversarial strategy shifts, scales favorably with the number of units

K

, and enables tunable multi-objective trade-offs. An extensive ablation study confirms the necessity of each core component. Our work establishes A3M as a powerful and flexible framework for learning in complex auction environments.

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Latest papers 83