Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

While Reinforcement Learning from Human Feedback (RLHF) aligns Large Language
Models (LLMs) with general, aggregate human preferences, it is suboptimal for learning …

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

Multi-objective reinforcement learning algorithms (MORL) extend standard reinforcement
learning (RL) to scenarios where agents must optimize multiple---potentially conflicting …

This article studies the trajectory control and task offloading (TCTO) problem in an
unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where a UAV flies …

Deep reinforcement learning (RL) has been applied extensively to solve complex decision-
making problems. In many real-world scenarios, tasks often have several conflicting …

This paper presents a novel approach to multiobjective algorithms aimed at modeling the
Pareto set using neural networks. Whereas previous methods mainly focused on identifying …

High entropy alloys (HEAs) are a highly promising class of materials for electrocatalysis as
their unique active site distributions break the scaling relations that limit the activity of …

In recent years, various companies have started to shift their data services from traditional
data centers to the cloud. One of the major motivations is to save on operational costs with …

Low Earth Orbit (LEO) satellites have emerged as crucial enablers of direct connections with
remote terrestrial terminals. However, energy limitations and insufficient antenna capabilities …