Demonstration Guided Multi-Objective Reinforcement Learning
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Abstract (arXiv)
Multi-objective reinforcement learning (MORL) is increasingly relevant due to its resemblance to real-world scenarios requiring trade-offs between multiple objectives. Catering to diverse user preferences, traditional reinforcement learning faces amplified challenges in MORL. To address the difficulty of training policies from scratch in MORL, we introduce demonstration-guided multi-objective reinforcement learning (DG-MORL). This novel approach utilizes prior demonstrations, aligns them with user preferences via corner weight support, and incorporates a self-evolving mechanism to refine suboptimal demonstrations. Our empirical studies demonstrate DG-MORL's superiority over existing MORL algorithms, establishing its robustness and efficacy, particularly under challenging conditions. We also provide an upper bound of the algorithm's sample complexity.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 61 references that are in the map. First posted on arXiv 5 Apr 2024.
Builds on
31 of 61 references.
References · 31
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19 more
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.