In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …

In mobile edge computing systems, an edge node may have a high load when a large
number of mobile devices offload their tasks to it. Those offloaded tasks may experience …

Reinforcement learning (RL) algorithms find applications in inventory control, recommender
systems, vehicular traffic management, cloud computing, and robotics. The real-world …

A trustworthy reinforcement learning algorithm should be competent in solving challenging
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …

With large-scale integration of renewable generation and distributed energy resources
(DERs), modern power systems are confronted with new operational challenges, such as …

Reinforcement learning algorithms, though successful, tend to over-fit to training
environments hampering their application to the real-world. This paper proposes $\text …

Most reinforcement learning methods are based upon the key assumption that the transition
dynamics and reward functions are fixed, that is, the underlying Markov decision process is …

Multi-modal computing (M 2 C) has recently exhibited impressive accuracy improvements in
numerous autonomous artificial intelligence of things (AIoT) systems. However, this …

Developing an optimal supervisory control policy for building energy management is a
complex problem because the system exhibits non-stationary behaviors, and the target …