This book considers large and challenging multistage decision problems, which can be
solved in principle by dynamic programming (DP), but their exact solution is computationally …

The purpose of this book is to develop in greater depth some of the methods from the
author's Reinforcement Learning and Optimal Control recently published textbook (Athena …

From household appliances to applications in robotics, engineered systems involving
complex dynamics can only be as effective as the algorithms that control them. While …

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 …

This is the 3rd edition of a research monograph providing a synthesis of old research on the
foundations of dynamic programming (DP), with the modern theory of approximate DP and …

This is the leading and most up-to-date textbook on the far-ranging algorithmic
methododogy of Dynamic Programming, which can be used for optimal control, Markovian …

An important application of reinforcement learning (RL) is to finite-state control problems and
one of the most difficult problems in learning for control is balancing the exploration …

The purpose of this book is to propose and develop a new conceptual framework for
approximate Dynamic Programming (DP) and Reinforcement Learning (RL). This framework …

Reinforcement learning (RL) is a set of mathematical methods and algorithms that can be
applied to a wide array of problems and plays a central role in machine learning. The aim of …

We introduce a class of MPDs which greatly simplify Reinforcement Learning. They have
discrete state spaces and continuous control spaces. The controls have the effect of …