Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned
on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further …

Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …

In many risk-aware and multi-objective reinforcement learning settings, the utility of the user
is derived from a single execution of a policy. In these settings, making decisions based on …

Artificial Intelligence (AI) has pervaded everyday life, reshaping the landscape of business,
economy, and society through the alteration of interactions and connections among …

Many real-world problems contain multiple objectives and agents, where a trade-off exists
between objectives. Key to solving such problems is to exploit sparse dependency …

This study presents a comparative analysis between single-objective and multi-objective
reinforcement learning methods for training a robot to navigate effectively to an end goal …