Handbook of Learning and Approximate Dynamic Programming
Jennie Si, Andrew G. Barto, Warren B. Powell, Donald C. Wunsch
Résumé
Approximate dynamic programming solves decision and control problems
While advances in science and engineering have enabled us to design and build complex systems, how to control and optimize them remains a challenge. This was made clear, for example, by the major power outage across dozens of cities in the Eastern United States and Canada in August of 2003. Learning and approximate dynamic programming (ADP) is emerging as one of the most promising mathematical and computational approaches to solve nonlinear, large-scale, dynamic control problems under uncertainty. It draws heavily both on rigorous mathematics and on biological inspiration and parallels, and helps unify new developments across many disciplines.
The foundations of learning and approximate dynamic programming have evolved from several fields-optimal control, artificial intelligence (reinforcement learning), operations research (dynamic programming), and stochastic approximation methods (neural networks). Applications of these methods span engineering, economics, business, and computer science. In this volume, leading experts in the field summarize the latest research in areas including:
- Reinforcement learning and its relationship to supervised learning
- Model-based adaptive critic designs
- Direct neural dynamic programming
- Hierarchical decision-making
- Multistage stochastic linear programming for resource allocation problems
- Concurrency, multiagency, and partial observability
- Backpropagation through time and derivative adaptive critics
- Applications of approximate dynamic programming and reinforcement learning in control-constrained agile missiles; power systems; heating, ventilation, and air conditioning; helicopter flight control; transportation and more.
L'auteur - Jennie Si
Jennie Si is Professor of Electrical Engineering, Arizona State University, Tempe, AZ. She is director of Intelligent Systems Laboratory, which focuses on analysis and design of learning and adaptive systems. In addition to her own publications, she is the Associate Editor for IEEE Transactions on Neural Networks, and past Associate Editor for IEEE Transactions on Automatic Control and IEEE Transactions on Semiconductor Manufacturing. She was the co-chair for the 2002 NSF Workshop on Learning and Approximate Dynamic Programming.
L'auteur - Andrew G. Barto
Andrew G. Barto is Professor of Computer Science, University of Massachusetts, Amherst. He is co-director of the Autonomous Learning Laboratory, which carries out interdisciplinary research on machine learning and modeling of biological learning. He is a core faculty member of the Neuroscience and Behavior Program of the University of Massachusetts and was the co-chair for the 2002 NSF Workshop on Learning and Approximate Dynamic Programming. He currently serves as an associate editor of Neural Computation.
L'auteur - Warren B. Powell
Warren B. Powell is Professor of Operations Research and Financial Engineering at Princeton University. He is director of CASTLE Laboratory, which focuses on real-time optimization of complex dynamic systems arising in transportation and logistics.
L'auteur - Donald C. Wunsch
Donald C. Wunsch is the Mary K. Finley Missouri Distinguished Professor in the Electrical and Computer
Sommaire
- ADP: Goals, opportunities and principles
- I: Overview
- Reinforcement learning and its relationship to supervised learning
- Model-based adaptative critic design
- Guidance in the use of adaptative critics control
- ...
- II. Technical advances
- Improved temporal difference methods with linear function approximation
- Approximate dynamic programming for high-dimensional resource allocation problems
- Hierarchical approaches to concurrency, multiagency, and partial observability
- ...
- III: Applications
- Near optimal control via reinforcement learning
- Multiobjective control problems by reinforcement learning
- Adaptative critic based neural network for control-constrained agile missile
- ...
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Wiley |
| Auteur(s) | Jennie Si, Andrew G. Barto, Warren B. Powell, Donald C. Wunsch |
| Parution | 24/08/2004 |
| Nb. de pages | 650 |
| Format | 16 x 24 |
| Couverture | Relié |
| Poids | 1055g |
| Intérieur | Noir et Blanc |
| EAN13 | 9780471660545 |
| ISBN13 | 978-0-471-66054-5 |
Avantages Eyrolles.com
Consultez aussi
- Les meilleures ventes en Graphisme & Photo
- Les meilleures ventes en Informatique
- Les meilleures ventes en Construction
- Les meilleures ventes en Entreprise & Droit
- Les meilleures ventes en Sciences
- Les meilleures ventes en Littérature
- Les meilleures ventes en Arts & Loisirs
- Les meilleures ventes en Vie pratique
- Les meilleures ventes en Voyage et Tourisme
- Les meilleures ventes en BD et Jeunesse
- Informatique Développement d'applications Algorithmique et informatique appliquée Graphes
- Informatique Développement d'applications Algorithmique et informatique appliquée Réseaux de neurones
- Informatique Développement d'applications Algorithmique et informatique appliquée Reconnaissance des formes
- Informatique Développement d'applications Algorithmique et informatique appliquée Recherche opérationnelle
- Informatique Développement d'applications Algorithmique et informatique appliquée Intelligence artificielle
- Sciences Mathématiques Mathématiques par matières Algèbre Algèbre linéaire
- Sciences Mathématiques Mathématiques appliquées Mathématiques pour la physique
- Sciences Mathématiques Mathématiques appliquées Statistiques
- Sciences Physique Physique fondamentale Systèmes dynamiques
- Sciences Etudes et concours Classes préparatoires et grandes écoles - Livres classes prépas scientifiques Physique
- Sciences Techniques Robotique
- Sciences Techniques Intelligence artificielle I.A. appliquée
- Sciences Techniques Intelligence artificielle Systèmes experts
- Sciences Techniques Intelligence artificielle Réseaux de neurones
- Sciences Techniques Automatique
