Reinforcement learning and approximate dynamic programming for feedback control
by Lewis, Frank L. Liu, Derong
"Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely gu…
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- Full title :Reinforcement learning and approximate dynamic programming for feedback control / edited by Frank L. Lewis, U TA Automation and Robotics Research Insitute, Derong Liu, University of Illinois, Chicago, IL.
Summary
"Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making"--
"Reinforcement learning and adaptive control can be useful for controlling a wide variety of systems including robots, industrial processes, and economical decision making"--
Edition Statements
- Publication Date:2013.
Publication Statements
- Publisher(s):IEEE Press, John Wiley and Sons, Inc
- Place of Publication:Piscataway, NJ
- Language:eng
- Publication Date:2013.
The Physical Object
- Description:xxvi, 613 pages : : illustrations ; : 24 cm
Edition Identifiers
- ISBN:9781118104200, 111810420X, 9781118453988, 1118453980
- oclc:ocn796758816
- LC Classification:Q325.6
- Dewey Decimal:003/.5
Table of Contents
Reinforcement learning and approximate dynamic programming (RLADP) : foundations, common misconceptions, and the challenges ahead / Paul J. Werbos -- Stable adaptive neural control of partially observable dynamic systems / J. Nate Knight, Charles W. Anderson -- Optimal control of unknown nonlinear discrete-time systems using the iterative globalized dual heuristic programming algorithm / Derong Liu, Ding Wang -- Learning and optimization in hierarchical adaptive critic design / Haibo He, Zhen Ni, Dongbin Zhao -- Single network adaptive critics networks : developments, analysis, and applications / Jie Ding, Ali Heydari, S.N. Balakrishnan -- Linearly solvable optimal control / K. Dvijotham, E. Todorov -- Approximating optimal control with value gradient learning / Michael Fairbank, Danil Prokhorov, Eduardo Alonso -- A constrained backpropagation approach to function approximation and approximate dynamic programming / Silvia Ferrari, Keith Rudd, Gianluca Di Muro -- Toward design of nonlinear ADP learning controllers with peformance assurance / Jennie Si [and others] -- Reinforcement learning control with time-dependent agent dynamics / Kenton Kirkpatrick, John Valasek -- Online optim…
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