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Scale-Invariant Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning
March 4, 2024, 5:41 a.m. | Dohyeong Kim, Mineui Hong, Jeongho Park, Songhwai Oh
cs.LG updates on arXiv.org arxiv.org
Abstract: Multi-objective reinforcement learning (MORL) aims to find a set of Pareto optimal policies to cover various preferences. However, to apply MORL in real-world applications, it is important to find policies that are not only Pareto optimal but also satisfy pre-defined constraints for safety. To this end, we propose a constrained MORL (CMORL) algorithm called Constrained Multi-Objective Gradient Aggregator (CoMOGA). Recognizing the difficulty of handling multiple objectives and constraints concurrently, CoMOGA relaxes the original CMORL problem …
abstract aggregation applications apply arxiv constraints cs.lg gradient multi-objective pareto reinforcement reinforcement learning safety scale set type world
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