The widespread penetration of inverter-based resources has profoundly impacted the
electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and …

Machine Unlearning, a pivotal field addressing data privacy in machine learning,
necessitates efficient methods for the removal of private or irrelevant data. In this context …

Intelligent robotic manipulation is a challenging study of machine intelligence. Although
many dexterous robotic hands have been designed to assist or replace human hands in …

Meta-reinforcement learning enables artificial agents to learn from related training tasks and
adapt to new tasks efficiently with minimal interaction data. However, most existing research …

Deep reinforcement learning (DRL) is a powerful tool for learning from interactions within a
stationary environment where state transition and reward distributions remain constant …

Reinforcement learning has been widely used for unmanned surface vehicle (USV) control
tasks. However, the requirement of numerous training samples limits its transferability to new …

Abstract “Open world” environments are those in which novel objects, agents, events, and
more can appear and contradict previous understandings of the environment. This runs …

Accurate models ensure the efficient and safe operations of industrial processes. However,
modeling of industrial processes with non-stationary characteristic is challenging. In this …

Introduction Behavioral Cloning (BC) is a common imitation learning method which utilizes
neural networks to approximate the demonstration action samples for task manipulation skill …

Although deep reinforcement learning has recently been very successful at learning
complex behaviors, it requires a tremendous amount of data to learn a task. One of the …