This paper surveys the opportunities and challenges in an emerging area of research that
has the potential to significantly ease the incorporation of renewable energy into the grid as …

A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …

Novel advanced policy gradient (APG) methods, such as trust region policy optimization and
proximal policy optimization (PPO), have become the dominant reinforcement learning …

Multi-agent reinforcement learning (MARL), despite its popularity and empirical success,
suffers from the curse of dimensionality. This paper builds the mathematical framework to …

Recently, unmanned aerial vehicle (UAV)-assisted wireless communication technology has
been proposed to exploit the favorable propagation property and flexibility of air-to-ground …

We study the convergence of Markov decision processes, composed of a large number of
objects, to optimization problems on ordinary differential equations. We show that the …

We investigate sensor transmission power control for remote state estimation. Instead of
using a conventional sensor, a sensor equipped with an energy harvester which can obtain …

Abstract In a M/M/N+ M queue, when there are many customers waiting, it may be preferable
to reject a new arrival rather than risk that arrival later abandoning without receiving service …

Home care provides personalized medical care and social support to patients within their
own homes. Our work proposes a dynamic scheduling framework to assist in the assignment …

Neuro‐dynamic programming encompasses techniques from both reinforcement learning
and approximate dynamic programming. Feature selection refers to the choice of basis that …