3 papers
cs.AI2026
Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning
Paolo Speziali, Arno De Greef, Mehrdad Asadi +3
We propose the Preference Guided Iterated Pareto Referent Optimisation (PG-IPRO) for urban route planning for people with different accessibility requirements and preferences. With…
cs.LG2026
Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance
Dimitris Michailidis, Willem Röpke, Diederik M. Roijers +2
Multi-Objective Reinforcement Learning (MORL) aims to learn a set of policies that optimize trade-offs between multiple, often conflicting objectives. MORL is computationally more…
cs.MA2024
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning
Florian Felten, Umut Ucak, Hicham Azmani +10
Many challenging tasks such as managing traffic systems, electricity grids, or supply chains involve complex decision-making processes that must balance multiple conflicting object…