  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.
