Performance optimization is vital in the design and operation of modern
engineering systems, including communications, manufacturing, robotics,
and logistics. Most engineering systems are too complicated to model, or
the system parameters cannot be easily identified, so learning
techniques have to be applied. This book provides a unified framework
based on a sensitivity point of view. It combines currently prominent
research on reinforcement learning / neuro-dynamic programming with a
unique research approach based on sensitivity analysis and
discrete-event systems concepts. This new perspective on a popular topic
is presented by a well respected expert in the field.