MORL
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Related Articles from SNS
Population-Free Pareto Tracking for Sample-Efficient Multi-Policy MORL
Announce Type: replace Abstract: Multi-objective reinforcement learning (MORL) is a fundamental framework for real-world decision-making problems involving multiple conflicting criteria. Existing multi-policy (MP) methods typically rely on online evolutionary frameworks that maintain large policy populations, leading to high sample complexity and excessive agent-environment interactions. To mitigate these limitations, we present Multi-policy Pareto Front Tracking (MPFT), a framework without...
Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion
arXiv:2605.31388v1 Announce Type: new Abstract: Multi-Objective Reinforcement Learning (MORL) extends standard RL by optimizing policies with respect to multiple, often conflicting, objectives. While max-min MORL has emerged as an effective approach for promoting fairness, its applicability remains limited, particularly when constraints must be incorporated. In this paper, we propose a MORL framework that integrates the max-min criterion with explicit constraint satisfaction.