Reinforcement learning and approximate dynamic programming for feedback control
by Lewis, Frank L. Liu, Derong
La 4e de couv. indique : "This book adresses the growing interest in reinforcement learning (RL) and adaptive dynamic programming (ADP) for controlling a wide variety of human-engineered systems relying on feedback control. It provides readers with a fascinating look at cutting-edge research and technologies, examines the latest RL and ADP techniques for decision and control, and illustrates the power and effectiveness of these methods for providing optimal control solutions in real time. Writt…
Read More ↓Title Statements
- Full title :Reinforcement learning and approximate dynamic programming for feedback control / edited by Frank L. Lewis,... Derong Liu,....
Summary
La 4e de couv. indique : "This book adresses the growing interest in reinforcement learning (RL) and adaptive dynamic programming (ADP) for controlling a wide variety of human-engineered systems relying on feedback control. It provides readers with a fascinating look at cutting-edge research and technologies, examines the latest RL and ADP techniques for decision and control, and illustrates the power and effectiveness of these methods for providing optimal control solutions in real time. Written by a diverse group of leading experts from around the world, Reinforcement learning and approximate dynamic programming for feedback control brings together current perspectives from many fields, including computational intelligence, automatic control systems, Markov decision processes, stochastic games, psychology, operations research, cybernetics, neural networks, and neurobiology. Clearly written and well organized, the book : develops methods for feedback control of systems based on RL and ADP, explains learning and control in multi-agent games as well as single-player games, outlines key approaches for implementing decision algorithms in Markov processes, incorporates numerous figures and simulation results throughout the text, offers guidance and ideas for future research and development. An essential guide for researchers in machine learning and automatic control systems, Reinforcement learning and approximate dynamic programming for feedback control is also useful for engineers in the automotive and aerospace industries, process control, and other areas."
Read More ↓Edition Statements
- Publication Date:cop. 2013.
Publication Statements
- Publisher(s):IEEE Press, J. Wiley & Sons
- Place of Publication:Hoboken (N.J.)
- Language:eng
- Publication Date:cop. 2013.
Notes
IEEE = Institute of Electrical and Electronics Engineers.
The Physical Object
- Description:1 vol. (xxvi-613 p.) : : graph., couv. ill. en coul. ; : 24 cm.
Edition Identifiers
- ISBN:9781118104200, 111810420X
- oclc:ocn867279811
- LC Classification:Q325.6
- Dewey Decimal:003/.5
Subjects
Writers
More from this Series
No reviews yet. Be the first to rate this work.
Related Works
more →
Reinforcement learning and stochastic optimization: a unified framework for sequential deci…
Powell, Warren B.
Human-robot interaction control using reinforcement learning
Yu, Wen, (Robotics engineer)
Statistical reinforcement learning: modern machine learning approaches
Sugiyama, Masashi
Multi-agent machine learning: a reinforcement approach
Multi-Agent Machine Learning
Files & Downloads
-
Reinforcement Learning and Approximate Dynamic Pr…
PDF · 44.5 MB · updated -
Reinforcement Learning and Approximate Dynamic Pr…
EPUB · 7.2 MB · updated
Rate & Review