Reinforcement learning and approximate dynamic programming for feedback control
Material type: TextSeries: IEEE Press series on computational intelligencePublication details: New Jersey: Wiley, c2013.Description: xxvi, 613 pages : illustrations ; 24 cmISBN:- 9781118104200
- 003.5 23 LEW-R
- Q325.6 .R464 2013
- TEC008000
Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|
Books | IIITD Reference | Computer Science and Engineering | REF 003.5 LEW-R (Browse shelf(Opens below)) | Not for loan | 003899 |
Browsing IIITD shelves, Shelving location: Reference, Collection: Computer Science and Engineering Close shelf browser (Hides shelf browser)
REF 003 EST-S The structure of complex networks : | REF 003 NEW-N Networks : | REF 003 NEW-N Networks | REF 003.5 LEW-R Reinforcement learning and approximate dynamic programming for feedback control | REF 003.5 XIO-B Bio-inspired computing and networking | REF 003.54 MID-N Non-Gaussian statistical communication theory | REF 003.54 RIC-M Modern coding theory |
"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"--
There are no comments on this title.