arXiv Machine Learning October 28, 2026 By Qiyue Xia, J. Michael Herrmann

Preference-Guided Cooperation in Multi-Objective Multi-Agent Reinforcement Learning

Read the original on arXiv Machine Learning →

The paper introduces Meta-Gradient Informed Multi-Objective Multi-Agent Reinforcement Learning (MIRL), a framework that leverages the structure of multiple objectives to enhance cooperation among agents. By formulating MOMARL at both team and individual levels and using objective-wise counterfactual credit assignment, MIRL allows agents to learn how to prioritize objectives individually while jointly optimizing a system-level objective. Experiments on a multi-objective particle environment demonstrate that MIRL, especially its dynamic variant, improves Pareto-front quality compared to baseline methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Aug 3

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback

arXiv:2607. 29559v1 Announce Type: new Abstract: Reinforcement Learning (RL) systems are typically trained using a single, well-specified scalar reward function.

By Manith Adikari, Bei Peng, Samuele Vinanzi, Angelo Cangelosi

agents reinforcement-learning benchmarks

arXiv AI

Jun 15

Learning Coordinated Preference for Multi-Objective Multi-Agent Reinforcement Learning

arXiv:2606. 14693v1 Announce Type: cross Abstract: Cooperative multi-objective multi-agent reinforcement learning (MOMARL) models team decision making under multiple, potentially conflicting objectives.

By Pengxin Wang, Lihao Guo, Yi Xie, Bo Liu, Siyang Cao, Jingdi Chen

agents reinforcement-learning

arXiv AI

Jul 13

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization

arXiv:2602. 07764v2 Announce Type: replace-cross Abstract: Multi-objective reinforcement learning (MORL) seeks to train agents capable of balancing conflicting objectives.

By Tanmay Ambadkar, Sourav Panda, Shreyash Kale, Jonathan Dodge, Abhinav Verma

agents reinforcement-learning benchmarks

arXiv AI

Jun 3

Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning

arXiv:2511. 02304v2 Announce Type: replace-cross Abstract: We study learning multi-task, multi-agent policies for cooperative, temporal objectives, under centralized training, decentralized execution.

By Beyazit Yalcinkaya, Marcell Vazquez-Chanlatte, Ameesh Shah, Hanna Krasowski, Sanjit A. Seshia

agents reinforcement-learning

arXiv AI

Jun 9

Counterfactual Credit Policy Optimization for Multi-Agent Collaboration

arXiv:2603. 21563v4 Announce Type: replace Abstract: Collaborative multi-agent large language models (LLMs) can solve complex reasoning tasks by decomposing roles, but reinforcement learning for such systems is limited by credit assignment: shared terminal rewards obscure individual contributions and can encourage free-riding.

By Zhongyi Li, Wan Tian, Yikun Ban, Jinju Chen, Huiming Zhang, Yang Liu, Fuzhen Zhuang

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Aug 11

Multi-Agent Reinforcement Learning via Agent-Specific Preference

arXiv:2608. 08604v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) is a powerful framework for solving complex collaborative tasks, but it relies heavily on well-defined global reward functions.

By Ni Mu, Yao Luan, Yiqin Yang, Qing-Shan Jia

agents reinforcement-learning benchmarks

One email a morning, machine-written

Related stories

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback

Learning Coordinated Preference for Multi-Objective Multi-Agent Reinforcement Learning

Preference Conditioned Multi-Objective Reinforcement Learning: Decomposed, Diversity-Driven Policy Optimization

Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning

Counterfactual Credit Policy Optimization for Multi-Agent Collaboration

Multi-Agent Reinforcement Learning via Agent-Specific Preference