On Generalization Across Environments In Multi-objective Reinforcement Learning

Abstract

Real-world sequential decision-making tasks often require balancing trade-offs between multiple conflicting objectives, making Multi-Objective Reinforcement Learning (MORL) an increasingly prominent field of research. Despite recent advances, existing MORL literature has narrowly focused on performance within static environments, neglecting the importance of generalizing across diverse settings. Conversely, existing research on generalization in RL has always assumed scalar rewards, overlooking the inherent multi-objectivity of real-world problems. Generalization in the multi-objective context is fundamentally more challenging, as it requires learning a Pareto set of policies addressing varying preferences across multiple objectives. In this paper, we formalize the concept of generalization in MORL and how it can be evaluated. We then contribute a novel benchmark featuring diverse multi-objective domains with parameterized environment configurations to facilitate future studies in this

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyteoh2025on

Related papers

• Addressing The Issue Of Stochastic Environments And Local Decision-making In Multi-objective Reinforcement Learning (2022)0.00
• A Generalized Algorithm For Multi-objective Reinforcement Learning And Policy Adaptation (2019)0.00
• Provable Multi-objective Reinforcement Learning With Generative Models (2020)0.00
• Multi-objective Reinforcement Learning Based On Decomposition: A Taxonomy And Framework (2023)9.92
• Utility-based Reinforcement Learning: Unifying Single-objective And Multi-objective Reinforcement Learning (2024)2.26
• Interpretability By Design For Efficient Multi-objective Reinforcement Learning (2025)0.00
• Navigating Trade-offs: Policy Summarization For Multi-objective Reinforcement Learning (2024)2.26
• Sample-efficient Multi-objective Learning Via Generalized Policy Improvement Prioritization (2023)5.24