Description
Taylor & Francis Ltd Reinforcement Learning And Dynamic Programming Using Function Approximators 2010 Edition by Lucian Busoniu
From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems.However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors' website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments. 1 Introduction The dynamic programming and reinforcement learning problem Approximation in dynamic programming and reinforcement learning About this book2 An introduction to dynamic programming and reinforcement learning Introduction Markov decision processesValue iterationPolicy iterationPolicy search Summary and discussion 3 Dynamic programming and reinforcement learning in large and continuousspaces Introduction The need for approximation in large and continuous spaces Approximation architectures Approximate value iteration Approximate policy iteration Finding value function approximators automatically Approximate policy searchComparison of approximate value iteration, policy iteration, and policy searchSummary and discussion 4 Approximate value iteration with a fuzzy representation Introduction Fuzzy Q-iteration Analysis of fuzzy Q-iteration Optimizing the membership functionsExperimental study Summary and discussion 5 Approximate policy iteration for online learning and continuous-action control Introduction A recapitulation of least-squares policy iterationOnline least-squares policy iteration Online LSPI with prior knowledge LSPI with continuous-action, polynomial approximation Experimental study Summary and discussion 6 Approximate policy search with cross-entropy optimization of basis functions Introduction Cross-entropy optimization Cross-entropy policy search Experimental study Summary and discussion Appendix A Extremely randomized trees Structure of the approximator Building and using a tree Appendix B The cross-entropy method Rare-event simulation using the cross-entropy methodCross-entropy optimization Symbols and abbreviations Bibliography List of algorithms Index