With extensive pretrained knowledge and high-level general capabilities, large language
models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in …

Building generalist agents that can accomplish many goals in rich open-ended
environments is one of the research frontiers for reinforcement learning. A key limiting factor …

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a
feedback loop with four core functionalities: monitoring, analyzing, planning, and execution …

Reward engineering has long been a challenge in Reinforcement Learning (RL) research,
as it often requires extensive human effort and iterative processes of trial-and-error to design …

Curriculum learning is a training mechanism in reinforcement learning (RL) that facilitates
the achievement of complex policies by progressively increasing the task difficulty during …

Human motion synthesis is a fundamental task in computer animation. Despite recent
progress in this field utilizing deep learning and motion capture data, existing methods are …