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2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning : Honolulu, HI, 1-5 April 2007
| 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning : Honolulu, HI, 1-5 April 2007 |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE, 2007 |
| Disciplina | 519.7/03 |
| Soggetto topico |
Dynamic programming
Machine learning Operations Research Civil & Environmental Engineering Engineering & Applied Sciences |
| ISBN | 1-5090-8720-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996201767103316 |
| [Place of publication not identified], : IEEE, 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning : Honolulu, HI, 1-5 April 2007
| 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning : Honolulu, HI, 1-5 April 2007 |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE, 2007 |
| Disciplina | 519.7/03 |
| Soggetto topico |
Dynamic programming
Machine learning Operations Research Civil & Environmental Engineering Engineering & Applied Sciences |
| ISBN |
9781509087204
1509087206 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910143037403321 |
| [Place of publication not identified], : IEEE, 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Approximate dynamic programming [[electronic resource] ] : solving the curses of dimensionality / / Warren B. Powell
| Approximate dynamic programming [[electronic resource] ] : solving the curses of dimensionality / / Warren B. Powell |
| Autore | Powell Warren B. <1955-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : J. Wiley & Sons, c2011 |
| Descrizione fisica | 1 online resource (658 p.) |
| Disciplina |
519.7/03
519.703 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Dynamic programming
Programming (Mathematics) |
| ISBN |
1-283-27370-5
9786613273703 1-118-02916-X 1-118-02917-8 1-118-02915-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Approximate Dynamic Programming; Contents; Preface to the Second Edition; Preface to the First Edition; Acknowledgments; 1 The Challenges of Dynamic Programming; 1.1 A Dynamic Programming Example: A Shortest Path Problem; 1.2 The Three Curses of Dimensionality; 1.3 Some Real Applications; 1.4 Problem Classes; 1.5 The Many Dialects of Dynamic Programming; 1.6 What Is New in This Book?; 1.7 Pedagogy; 1.8 Bibliographic Notes; 2 Some Illustrative Models; 2.1 Deterministic Problems; 2.2 Stochastic Problems; 2.3 Information Acquisition Problems; 2.4 A Simple Modeling Framework for Dynamic Programs
2.5 Bibliographic NotesProblems; 3 Introduction to Markov Decision Processes; 3.1 The Optimality Equations; 3.2 Finite Horizon Problems; 3.3 Infinite Horizon Problems; 3.4 Value Iteration; 3.5 Policy Iteration; 3.6 Hybrid Value-Policy Iteration; 3.7 Average Reward Dynamic Programming; 3.8 The Linear Programming Method for Dynamic Programs; 3.9 Monotone Policies*; 3.10 Why Does It Work?**; 3.11 Bibliographic Notes; Problems; 4 Introduction to Approximate Dynamic Programming; 4.1 The Three Curses of Dimensionality (Revisited); 4.2 The Basic Idea; 4.3 Q-Learning and SARSA 4.4 Real-Time Dynamic Programming4.5 Approximate Value Iteration; 4.6 The Post-Decision State Variable; 4.7 Low-Dimensional Representations of Value Functions; 4.8 So Just What Is Approximate Dynamic Programming?; 4.9 Experimental Issues; 4.10 But Does It Work?; 4.11 Bibliographic Notes; Problems; 5 Modeling Dynamic Programs; 5.1 Notational Style; 5.2 Modeling Time; 5.3 Modeling Resources; 5.4 The States of Our System; 5.5 Modeling Decisions; 5.6 The Exogenous Information Process; 5.7 The Transition Function; 5.8 The Objective Function; 5.9 A Measure-Theoretic View of Information** 5.10 Bibliographic NotesProblems; 6 Policies; 6.1 Myopic Policies; 6.2 Lookahead Policies; 6.3 Policy Function Approximations; 6.4 Value Function Approximations; 6.5 Hybrid Strategies; 6.6 Randomized Policies; 6.7 How to Choose a Policy?; 6.8 Bibliographic Notes; Problems; 7 Policy Search; 7.1 Background; 7.2 Gradient Search; 7.3 Direct Policy Search for Finite Alternatives; 7.4 The Knowledge Gradient Algorithm for Discrete Alternatives; 7.5 Simulation Optimization; 7.6 Why Does It Work?**; 7.7 Bibliographic Notes; Problems; 8 Approximating Value Functions; 8.1 Lookup Tables and Aggregation 8.2 Parametric Models8.3 Regression Variations; 8.4 Nonparametric Models; 8.5 Approximations and the Curse of Dimensionality; 8.6 Why Does It Work?**; 8.7 Bibliographic Notes; Problems; 9 Learning Value Function Approximations; 9.1 Sampling the Value of a Policy; 9.2 Stochastic Approximation Methods; 9.3 Recursive Least Squares for Linear Models; 9.4 Temporal Difference Learning with a Linear Model; 9.5 Bellman's Equation Using a Linear Model; 9.6 Analysis of TD(0), LSTD, and LSPE Using a Single State; 9.7 Gradient-Based Methods for Approximate Value Iteration* 9.8 Least Squares Temporal Differencing with Kernel Regression* |
| Record Nr. | UNINA-9910139593803321 |
Powell Warren B. <1955->
|
||
| Hoboken, N.J., : J. Wiley & Sons, c2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Approximate dynamic programming : solving the curses of dimensionality / / Warren B. Powell
| Approximate dynamic programming : solving the curses of dimensionality / / Warren B. Powell |
| Autore | Powell Warren B. <1955-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Hoboken, N.J., : J. Wiley & Sons, c2011 |
| Descrizione fisica | 1 online resource (658 p.) |
| Disciplina | 519.7/03 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Dynamic programming
Programming (Mathematics) |
| ISBN |
9786613273703
9781283273701 1283273705 9781118029169 111802916X 9781118029176 1118029178 9781118029152 1118029151 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Approximate Dynamic Programming; Contents; Preface to the Second Edition; Preface to the First Edition; Acknowledgments; 1 The Challenges of Dynamic Programming; 1.1 A Dynamic Programming Example: A Shortest Path Problem; 1.2 The Three Curses of Dimensionality; 1.3 Some Real Applications; 1.4 Problem Classes; 1.5 The Many Dialects of Dynamic Programming; 1.6 What Is New in This Book?; 1.7 Pedagogy; 1.8 Bibliographic Notes; 2 Some Illustrative Models; 2.1 Deterministic Problems; 2.2 Stochastic Problems; 2.3 Information Acquisition Problems; 2.4 A Simple Modeling Framework for Dynamic Programs
2.5 Bibliographic NotesProblems; 3 Introduction to Markov Decision Processes; 3.1 The Optimality Equations; 3.2 Finite Horizon Problems; 3.3 Infinite Horizon Problems; 3.4 Value Iteration; 3.5 Policy Iteration; 3.6 Hybrid Value-Policy Iteration; 3.7 Average Reward Dynamic Programming; 3.8 The Linear Programming Method for Dynamic Programs; 3.9 Monotone Policies*; 3.10 Why Does It Work?**; 3.11 Bibliographic Notes; Problems; 4 Introduction to Approximate Dynamic Programming; 4.1 The Three Curses of Dimensionality (Revisited); 4.2 The Basic Idea; 4.3 Q-Learning and SARSA 4.4 Real-Time Dynamic Programming4.5 Approximate Value Iteration; 4.6 The Post-Decision State Variable; 4.7 Low-Dimensional Representations of Value Functions; 4.8 So Just What Is Approximate Dynamic Programming?; 4.9 Experimental Issues; 4.10 But Does It Work?; 4.11 Bibliographic Notes; Problems; 5 Modeling Dynamic Programs; 5.1 Notational Style; 5.2 Modeling Time; 5.3 Modeling Resources; 5.4 The States of Our System; 5.5 Modeling Decisions; 5.6 The Exogenous Information Process; 5.7 The Transition Function; 5.8 The Objective Function; 5.9 A Measure-Theoretic View of Information** 5.10 Bibliographic NotesProblems; 6 Policies; 6.1 Myopic Policies; 6.2 Lookahead Policies; 6.3 Policy Function Approximations; 6.4 Value Function Approximations; 6.5 Hybrid Strategies; 6.6 Randomized Policies; 6.7 How to Choose a Policy?; 6.8 Bibliographic Notes; Problems; 7 Policy Search; 7.1 Background; 7.2 Gradient Search; 7.3 Direct Policy Search for Finite Alternatives; 7.4 The Knowledge Gradient Algorithm for Discrete Alternatives; 7.5 Simulation Optimization; 7.6 Why Does It Work?**; 7.7 Bibliographic Notes; Problems; 8 Approximating Value Functions; 8.1 Lookup Tables and Aggregation 8.2 Parametric Models8.3 Regression Variations; 8.4 Nonparametric Models; 8.5 Approximations and the Curse of Dimensionality; 8.6 Why Does It Work?**; 8.7 Bibliographic Notes; Problems; 9 Learning Value Function Approximations; 9.1 Sampling the Value of a Policy; 9.2 Stochastic Approximation Methods; 9.3 Recursive Least Squares for Linear Models; 9.4 Temporal Difference Learning with a Linear Model; 9.5 Bellman's Equation Using a Linear Model; 9.6 Analysis of TD(0), LSTD, and LSPE Using a Single State; 9.7 Gradient-Based Methods for Approximate Value Iteration* 9.8 Least Squares Temporal Differencing with Kernel Regression* |
| Record Nr. | UNINA-9910822581503321 |
|
Powell Warren B. <1955->
|
||
| Hoboken, N.J., : J. Wiley & Sons, c2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Dynamic programming : foundations and principles / / Moshe Sniedovich
| Dynamic programming : foundations and principles / / Moshe Sniedovich |
| Autore | Sniedovich Moshe <1945-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Boca Raton : , : CRC Press, , 2010 |
| Descrizione fisica | 1 online resource (616 p.) |
| Disciplina | 519.7/03 |
| Collana | Pure and applied mathematics |
| Soggetto topico |
Dynamic programming
Programming (Mathematics) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-429-11620-9
1-282-90218-0 9786612902185 1-4200-1463-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front cover; Preface (first edition); List of Figures; List of Tables; Contents; Chapter 1. Introduction; Chapter 2. Fundamentals; Chapter 3. Multistage Decision Model; Chapter 4. Dynamic Programming - An Outline; Chapter 5. Solution Methods; Chapter 6. Successive Approximation Methods; Chapter 7. Optimal Policies; Chpater 8. The Curse of Dimensionality; Chapter 9. The Rest Is Mathematics and Experience; Chapter 10. Refinements; Chapter 11. The State; Chapter 12. Parametric Schemes; Chapter 13. The Principle of Optimality; Chapter 14. Forward Decomposition; Chapter 15. Push!
Chapter 16. What Then Is Dynamic Programming?Appendix A. Contraction Mapping; Appendix B. Fractional Programming; Appendix C. Composite Concave Programming; Appendix D. The Principle of Optimality in Stochastic Processes; Appendix E. The Corridor Method; Bibliography; Back cover |
| Record Nr. | UNINA-9910459543403321 |
|
Sniedovich Moshe <1945->
|
||
| Boca Raton : , : CRC Press, , 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Dynamic programming : foundations and principles / / Moshe Sniedovich
| Dynamic programming : foundations and principles / / Moshe Sniedovich |
| Autore | Sniedovich Moshe <1945-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Boca Raton : , : CRC Press, , 2010 |
| Descrizione fisica | 1 online resource (616 p.) |
| Disciplina | 519.7/03 |
| Collana | Pure and applied mathematics |
| Soggetto topico |
Dynamic programming
Programming (Mathematics) |
| ISBN |
0-429-11620-9
1-282-90218-0 9786612902185 1-4200-1463-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front cover; Preface (first edition); List of Figures; List of Tables; Contents; Chapter 1. Introduction; Chapter 2. Fundamentals; Chapter 3. Multistage Decision Model; Chapter 4. Dynamic Programming - An Outline; Chapter 5. Solution Methods; Chapter 6. Successive Approximation Methods; Chapter 7. Optimal Policies; Chpater 8. The Curse of Dimensionality; Chapter 9. The Rest Is Mathematics and Experience; Chapter 10. Refinements; Chapter 11. The State; Chapter 12. Parametric Schemes; Chapter 13. The Principle of Optimality; Chapter 14. Forward Decomposition; Chapter 15. Push!
Chapter 16. What Then Is Dynamic Programming?Appendix A. Contraction Mapping; Appendix B. Fractional Programming; Appendix C. Composite Concave Programming; Appendix D. The Principle of Optimality in Stochastic Processes; Appendix E. The Corridor Method; Bibliography; Back cover |
| Record Nr. | UNINA-9910785135103321 |
|
Sniedovich Moshe <1945->
|
||
| Boca Raton : , : CRC Press, , 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Dynamic programming : foundations and principles / / Moshe Sniedovich
| Dynamic programming : foundations and principles / / Moshe Sniedovich |
| Autore | Sniedovich Moshe <1945-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Boca Raton, : CRC Press, 2010 |
| Descrizione fisica | 1 online resource (616 p.) |
| Disciplina | 519.7/03 |
| Collana | Pure and applied mathematics |
| Soggetto topico |
Dynamic programming
Programming (Mathematics) |
| ISBN |
0-429-11620-9
1-282-90218-0 9786612902185 1-4200-1463-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front cover; Preface (first edition); List of Figures; List of Tables; Contents; Chapter 1. Introduction; Chapter 2. Fundamentals; Chapter 3. Multistage Decision Model; Chapter 4. Dynamic Programming - An Outline; Chapter 5. Solution Methods; Chapter 6. Successive Approximation Methods; Chapter 7. Optimal Policies; Chpater 8. The Curse of Dimensionality; Chapter 9. The Rest Is Mathematics and Experience; Chapter 10. Refinements; Chapter 11. The State; Chapter 12. Parametric Schemes; Chapter 13. The Principle of Optimality; Chapter 14. Forward Decomposition; Chapter 15. Push!
Chapter 16. What Then Is Dynamic Programming?Appendix A. Contraction Mapping; Appendix B. Fractional Programming; Appendix C. Composite Concave Programming; Appendix D. The Principle of Optimality in Stochastic Processes; Appendix E. The Corridor Method; Bibliography; Back cover |
| Record Nr. | UNINA-9910968271603321 |
|
Sniedovich Moshe <1945->
|
||
| Boca Raton, : CRC Press, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Handbook of learning and approximate dynamic programming / / [edited by] Jennie Si ... [et al.]
| Handbook of learning and approximate dynamic programming / / [edited by] Jennie Si ... [et al.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : IEEE Press, , c2004 |
| Descrizione fisica | 1 PDF (xxi, 644 pages) : illustrations |
| Disciplina | 519.7/03 |
| Altri autori (Persone) | SiJennie |
| Collana | IEEE press series on computational intelligence |
| Soggetto topico |
Dynamic programming
Automatic programming (Computer science) Machine learning Control theory Systems engineering Engineering & Applied Sciences Civil & Environmental Engineering Computer Science Operations Research |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Foreword. -- 1. ADP: goals, opportunities and principles. -- Part I: Overview. -- 2. Reinforcement learning and its relationship to supervised learning. -- 3. Model-based adaptive critic designs. -- 4. Guidance in the use of adaptive critics for control. -- 5. Direct neural dynamic programming. -- 6. The linear programming approach to approximate dynamic programming. -- 7. Reinforcement learning in large, high-dimensional state spaces. -- 8. Hierarchical decision making. -- Part II: Technical advances. -- 9. Improved temporal difference methods with linear function approximation. -- 10. Approximate dynamic programming for high-dimensional resource allocation problems. -- 11. Hierarchical approaches to concurrency, multiagency, and partial observability. -- 12. Learning and optimization - from a system theoretic perspective. -- 13. Robust reinforcement learning using integral-quadratic constraints. -- 14. Supervised actor-critic reinforcement learning. -- 15. BPTT and DAC - a common framework for comparison. -- Part III: Applications. -- 16. Near-optimal control via reinforcement learning. -- 17. Multiobjective control problems by reinforcement learning. -- 18. Adaptive critic based neural network for control-constrained agile missile. -- 19. Applications of approximate dynamic programming in power systems control. -- 20. Robust reinforcement learning for heating, ventilation, and air conditioning control of buildings. -- 21. Helicopter flight control using direct neural dynamic programming. -- 22. Toward dynamic stochastic optimal power flow. -- 23. Control, optimization, security, and self-healing of benchmark power systems. |
| Record Nr. | UNINA-9910133842403321 |
| Hoboken, New Jersey : , : IEEE Press, , c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Handbook of learning and approximate dynamic programming / / [edited by] Jennie Si ... [et al.]
| Handbook of learning and approximate dynamic programming / / [edited by] Jennie Si ... [et al.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : IEEE Press, , c2004 |
| Descrizione fisica | 1 PDF (xxi, 644 pages) : illustrations |
| Disciplina | 519.7/03 |
| Altri autori (Persone) | SiJennie |
| Collana | IEEE press series on computational intelligence |
| Soggetto topico |
Dynamic programming
Automatic programming (Computer science) Machine learning Control theory Systems engineering Engineering & Applied Sciences Civil & Environmental Engineering Computer Science Operations Research |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Foreword. -- 1. ADP: goals, opportunities and principles. -- Part I: Overview. -- 2. Reinforcement learning and its relationship to supervised learning. -- 3. Model-based adaptive critic designs. -- 4. Guidance in the use of adaptive critics for control. -- 5. Direct neural dynamic programming. -- 6. The linear programming approach to approximate dynamic programming. -- 7. Reinforcement learning in large, high-dimensional state spaces. -- 8. Hierarchical decision making. -- Part II: Technical advances. -- 9. Improved temporal difference methods with linear function approximation. -- 10. Approximate dynamic programming for high-dimensional resource allocation problems. -- 11. Hierarchical approaches to concurrency, multiagency, and partial observability. -- 12. Learning and optimization - from a system theoretic perspective. -- 13. Robust reinforcement learning using integral-quadratic constraints. -- 14. Supervised actor-critic reinforcement learning. -- 15. BPTT and DAC - a common framework for comparison. -- Part III: Applications. -- 16. Near-optimal control via reinforcement learning. -- 17. Multiobjective control problems by reinforcement learning. -- 18. Adaptive critic based neural network for control-constrained agile missile. -- 19. Applications of approximate dynamic programming in power systems control. -- 20. Robust reinforcement learning for heating, ventilation, and air conditioning control of buildings. -- 21. Helicopter flight control using direct neural dynamic programming. -- 22. Toward dynamic stochastic optimal power flow. -- 23. Control, optimization, security, and self-healing of benchmark power systems. |
| Record Nr. | UNISA-996216868103316 |
| Hoboken, New Jersey : , : IEEE Press, , c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Handbook of learning and approximate dynamic programming / / [edited by] Jennie Si ... [et al.]
| Handbook of learning and approximate dynamic programming / / [edited by] Jennie Si ... [et al.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : IEEE Press, , c2004 |
| Descrizione fisica | 1 PDF (xxi, 644 pages) : illustrations |
| Disciplina | 519.7/03 |
| Altri autori (Persone) | SiJennie |
| Collana | IEEE press series on computational intelligence |
| Soggetto topico |
Dynamic programming
Automatic programming (Computer science) Machine learning Control theory Systems engineering Engineering & Applied Sciences Civil & Environmental Engineering Computer Science Operations Research |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Foreword. -- 1. ADP: goals, opportunities and principles. -- Part I: Overview. -- 2. Reinforcement learning and its relationship to supervised learning. -- 3. Model-based adaptive critic designs. -- 4. Guidance in the use of adaptive critics for control. -- 5. Direct neural dynamic programming. -- 6. The linear programming approach to approximate dynamic programming. -- 7. Reinforcement learning in large, high-dimensional state spaces. -- 8. Hierarchical decision making. -- Part II: Technical advances. -- 9. Improved temporal difference methods with linear function approximation. -- 10. Approximate dynamic programming for high-dimensional resource allocation problems. -- 11. Hierarchical approaches to concurrency, multiagency, and partial observability. -- 12. Learning and optimization - from a system theoretic perspective. -- 13. Robust reinforcement learning using integral-quadratic constraints. -- 14. Supervised actor-critic reinforcement learning. -- 15. BPTT and DAC - a common framework for comparison. -- Part III: Applications. -- 16. Near-optimal control via reinforcement learning. -- 17. Multiobjective control problems by reinforcement learning. -- 18. Adaptive critic based neural network for control-constrained agile missile. -- 19. Applications of approximate dynamic programming in power systems control. -- 20. Robust reinforcement learning for heating, ventilation, and air conditioning control of buildings. -- 21. Helicopter flight control using direct neural dynamic programming. -- 22. Toward dynamic stochastic optimal power flow. -- 23. Control, optimization, security, and self-healing of benchmark power systems. |
| Record Nr. | UNINA-9910829994103321 |
| Hoboken, New Jersey : , : IEEE Press, , c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
