Firms increasingly deploy algorithmic pricing approaches to determine what to charge for
their goods and services. Algorithmic pricing can discriminate prices both dynamically over …

In the last few years, reinforcement learning (RL), also called adaptive (or approximate)
dynamic programming, has emerged as a powerful tool for solving complex sequential …

Reinforcement learning is a learning paradigm concerned with learning to control a system
so as to maximize a numerical performance measure that expresses a long-term objective …

Preventive maintenance and production scheduling are two important and interactive
activities in production systems. In this work, the integrated optimization problem of …

This book is written for students and researchers in the field of industrial engineering,
computer science, operations research, management science, electrical engineering, and …

The manufacturing world is subject to ever-increasing cost optimization pressures.
Maintenance adds to cost and disrupts production; optimized maintenance is therefore of …

The Internet of Things (IoT) connects a huge number of resource-constraint IoT devices to
the Internet, which generate massive amount of data that can be offloaded to the cloud for …

We introduce learning and planning algorithms for average-reward MDPs, including 1) the
first general proven-convergent off-policy model-free control algorithm without reference …

The study of new medical treatments, and sequences of treatments, is inextricably linked
with statistics. Without statistical estimation and inference, we are left with case studies and …

In recent years, the research on reinforcement learning (RL) has focused on function
approximation in learning prediction and control of Markov decision processes (MDPs). The …