arxiv
PublishedJune 17, 2026 at 4:00 AM
—neutral
Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning
Publisher summary· verbatim
arXiv:2606.18111v1 Announce Type: cross Abstract: Fairness is an important aspect of decision-making in multi-objective reinforcement learning (MORL), where policies must ensure both optimality and equity across multiple, potentially conflicting objectives. While single-policy MORL methods can learn
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivCompMat-Bench: Benchmarking AI Agents for Computational Materials Science6harxivLLMR: Knowledge Distillation with a Large Language Model-Induced Reward6harxivPRISM: PRIvileged Sibling-guided Multi-Agent Topology Design6harxivTraining Reasoning Models to Generate Diverse Scientific Ideas6hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗