Handbook of learning and approximate dynamic programming. Si, J. IEEE Press, 2004.
Handbook of learning and approximate dynamic programming [link]Website  abstract   bibtex   
. A complete resource to Approximate Dynamic Programming (ADP), including on-line simulation code. Provides a tutorial that readers can use to start implementing the learning algorithms provided in the book. Includes ideas, directions, and recent results on current research issues and addresses applications where ADP has been successfully implemented. The contributors are leading researchers in the field. Front Matter -- Foreword / Shankar Sastry -- ADP: Goals, Opportunities and Principles / Paul Werbos -- Overview. Reinforcement Learning and Its Relationship to Supervised Learning / Andrew G Barto, Thomas G Dietterich -- Model-Based Adaptive Critic Designs / Silvia Ferrari, Robert F Stengel -- Guidance in the Use of Adaptive Critics for Control / George G Lendaris, James C Neidhoefer -- Direct Neural Dynamic Programming / Jennie Si, Lei Yang, Derong Liu -- The Linear Programming Approach to Approximate Dynamic Programming / Daniela Pucci de Farias -- Reinforcement Learning in Large, High-Dimensional State Spaces / Greg Grudic, Lyle Ungar -- Hierarchical Decision Making / Malcolm Ryan -- Technical Advances. Improved Temporal Difference Methods with Linear Function Approximation / Dimitri P Bertsekas, Vivek S Borkar, Angelia Nedich -- Approximate Dynamic Programming for High-Dimensional Resource Allocation Problems / Warren B Powell, Benjamin Van Roy -- Hierarchical Approaches to Concurrency, Multiagency, and Partial Observability / Sridhar Mahadevan, Mohammad Ghavamzadeh, Khashayar Rohanimanesh, Georgias Theocharous -- Learning and Optimization -- From a System Theoretic Perspective / Xi-Ren Cao -- Robust Reinforcement Learning Using Integral-Quadratic Constraints / Charles W Anderson, Matt Kretchmar, Peter Young, Douglas Hittle -- Supervised Actor-Critic Reinforcement Learning / Michael T Rosenstein, Andrew G Barto -- BPTT and DAC -- A Common Framework for Comparison / Danil V Prokhorov -- Applications. Near-Optimal Control Via Reinforcement Learning and Hybridization / Augustine O Esogbue, Warren E Hearnes -- Multiobjective Control Problems by Reinforcement Learning / Dong-Oh Kang, Zeungnam Bien -- Adaptive Critic Based Neural Network for Control-Constrained Agile Missile / S N Balakrishnan, Dongchen Han -- Applications of Approximate Dynamic Programming in Power Systems Control / Ganesh K Venayagamoorthy, Donald C Wunsch, Ronald G Harley -- Robust Reinforcement Learning for Heating, Ventilation, and Air Conditioning Control of Buildings / Charles W Anderson, Douglas Hittle, Matt Kretchmar, Peter Young -- Helicopter Flight Control Using Direct Neural Dynamic Programming / Russell Enns, Jennie Si -- Toward Dynamic Stochastic Optimal Power Flow / James A Momoh -- Control, Optimization, Security, and Self-healing of Benchmark Power Systems / James A Momoh, Edwin Zivi -- Bibliography -- Index.
@book{
 title = {Handbook of learning and approximate dynamic programming},
 type = {book},
 year = {2004},
 identifiers = {[object Object]},
 pages = {644},
 websites = {https://www.wiley.com/en-us/Handbook+of+Learning+and+Approximate+Dynamic+Programming-p-9780471660545},
 publisher = {IEEE Press},
 id = {d4ce77a3-9e37-3366-a486-7f7ba0062aa5},
 created = {2018-02-14T18:02:22.100Z},
 accessed = {2018-02-14},
 file_attached = {false},
 profile_id = {94cd643a-5276-3417-96f0-5dae1d90e4ae},
 group_id = {52cd5289-1f3c-3359-b00d-021cbc0b1164},
 last_modified = {2018-02-14T18:02:22.100Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {. A complete resource to Approximate Dynamic Programming (ADP), including on-line simulation code. Provides a tutorial that readers can use to start implementing the learning algorithms provided in the book. Includes ideas, directions, and recent results on current research issues and addresses applications where ADP has been successfully implemented. The contributors are leading researchers in the field. Front Matter -- Foreword / Shankar Sastry -- ADP: Goals, Opportunities and Principles / Paul Werbos -- Overview. Reinforcement Learning and Its Relationship to Supervised Learning / Andrew G Barto, Thomas G Dietterich -- Model-Based Adaptive Critic Designs / Silvia Ferrari, Robert F Stengel -- Guidance in the Use of Adaptive Critics for Control / George G Lendaris, James C Neidhoefer -- Direct Neural Dynamic Programming / Jennie Si, Lei Yang, Derong Liu -- The Linear Programming Approach to Approximate Dynamic Programming / Daniela Pucci de Farias -- Reinforcement Learning in Large, High-Dimensional State Spaces / Greg Grudic, Lyle Ungar -- Hierarchical Decision Making / Malcolm Ryan -- Technical Advances. Improved Temporal Difference Methods with Linear Function Approximation / Dimitri P Bertsekas, Vivek S Borkar, Angelia Nedich -- Approximate Dynamic Programming for High-Dimensional Resource Allocation Problems / Warren B Powell, Benjamin Van Roy -- Hierarchical Approaches to Concurrency, Multiagency, and Partial Observability / Sridhar Mahadevan, Mohammad Ghavamzadeh, Khashayar Rohanimanesh, Georgias Theocharous -- Learning and Optimization -- From a System Theoretic Perspective / Xi-Ren Cao -- Robust Reinforcement Learning Using Integral-Quadratic Constraints / Charles W Anderson, Matt Kretchmar, Peter Young, Douglas Hittle -- Supervised Actor-Critic Reinforcement Learning / Michael T Rosenstein, Andrew G Barto -- BPTT and DAC -- A Common Framework for Comparison / Danil V Prokhorov -- Applications. Near-Optimal Control Via Reinforcement Learning and Hybridization / Augustine O Esogbue, Warren E Hearnes -- Multiobjective Control Problems by Reinforcement Learning / Dong-Oh Kang, Zeungnam Bien -- Adaptive Critic Based Neural Network for Control-Constrained Agile Missile / S N Balakrishnan, Dongchen Han -- Applications of Approximate Dynamic Programming in Power Systems Control / Ganesh K Venayagamoorthy, Donald C Wunsch, Ronald G Harley -- Robust Reinforcement Learning for Heating, Ventilation, and Air Conditioning Control of Buildings / Charles W Anderson, Douglas Hittle, Matt Kretchmar, Peter Young -- Helicopter Flight Control Using Direct Neural Dynamic Programming / Russell Enns, Jennie Si -- Toward Dynamic Stochastic Optimal Power Flow / James A Momoh -- Control, Optimization, Security, and Self-healing of Benchmark Power Systems / James A Momoh, Edwin Zivi -- Bibliography -- Index.},
 bibtype = {book},
 author = {Si, Jennie.}
}

Downloads: 0