AI RESEARCH
Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance
arXiv CS.LG
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ArXi:2411.18195v2 Announce Type: replace Multi-Objective Reinforcement Learning (MORL) aims to learn a set of policies that optimize trade-offs between multiple, often conflicting objectives. MORL is computationally complex than single-objective RL, particularly as the number of objectives increases. Additionally, when objectives involve the preferences of agents or groups, incorporating fairness becomes both important and socially desirable. This paper