This paper surveys the field of reinforcement learning from a computer-science perspective.
It is written to be accessible to researchers familiar with machine learning. Both the historical …

A new edition of a graduate-level machine learning textbook that focuses on the analysis
and theory of algorithms. This book is a general introduction to machine learning that can …

The significantly expanded and updated new edition of a widely used text on reinforcement
learning, one of the most active research areas in artificial intelligence. Reinforcement …

This thesis is a detailed investigation into the following question: how much data must an
agent collect in order to perform" reinforcement learning" successfully? This question is …

This paper presents a detailed study of average reward reinforcement learning, an
undiscounted optimality framework that is more appropriate for cyclical tasks than the much …

This paper presents a skill learning model CLARION. Different from existing models of
mostly high‐level skill learning that use a top‐down approach (that is, turning declarative …

Sequential decision making is a fundamental task faced by any intelligent agent in an
extended interaction with its environment; it is the act of answering the question" What …

This dissertation considers a particular aspect of sequential decision making under
uncertainty in which, at each stage, a decision-making agent operating in an uncertain world …

This book is a condensation of a large body of work concerning human learning carried out
over a period of more than five years by Dr. Sun and his collaborators. In a nutshell, this …

The Handbook of Neural Computation is a practical, hands-on guide to the design and
implementation of neural networks used by scientists and engineers to tackle difficult and/or …