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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
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-9910822581503321 |
|
Powell Warren B. <1955->
|
||
| Hoboken, N.J., : J. Wiley & Sons, c2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimal learning [[electronic resource] /] / Warren B. Powell, Ilya O. Ryzhov
| Optimal learning [[electronic resource] /] / Warren B. Powell, Ilya O. Ryzhov |
| Autore | Powell Warren B. <1955-> |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2012 |
| Descrizione fisica | 1 online resource (416 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | RyzhovIlya Olegovich <1985-> |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Machine learning
Artificial intelligence |
| ISBN |
1-118-30984-7
1-280-59249-4 9786613622327 1-118-30453-5 1-118-30982-0 1-118-30985-5 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Optimal Learning; CONTENTS; Preface; Acknowledgments; 1 The Challenges of Learning; 1.1 Learning the Best Path; 1.2 Areas of Application; 1.3 Major Problem Classes; 1.4 The Different Types of Learning; 1.5 Learning from Different Communities; 1.6 Information Collection Using Decision Trees; 1.6.1 A Basic Decision Tree; 1.6.2 Decision Tree for Offline Learning; 1.6.3 Decision Tree for Online Learning; 1.6.4 Discussion; 1.7 Website and Downloadable Software; 1.8 Goals of this Book; Problems; 2 Adaptive Learning; 2.1 The Frequentist View; 2.2 The Bayesian View
2.2.1 The Updating Equations for Independent Beliefs2.2.2 The Expected Value of Information; 2.2.3 Updating for Correlated Normal Priors; 2.2.4 Bayesian Updating with an Uninformative Prior; 2.3 Updating for Non-Gaussian Priors; 2.3.1 The Gamma-Exponential Model; 2.3.2 The Gamma-Poisson Model; 2.3.3 The Pareto-Uniform Model; 2.3.4 Models for Learning Probabilities*; 2.3.5 Learning an Unknown Variance*; 2.4 Monte Carlo Simulation; 2.5 Why Does It Work?*; 2.5.1 Derivation of σ; 2.5.2 Derivation of Bayesian Updating Equations for Independent Beliefs; 2.6 Bibliographic Notes; Problems 3 The Economics of Information3.1 An Elementary Information Problem; 3.2 The Marginal Value of Information; 3.3 An information Acquisition Problem; 3.4 Bibliographic Notes; Problems; 4 Ranking and Selection; 4.1 The Model; 4.2 Measurement Policies; 4.2.1 Deterministic Versus Sequential Policies; 4.2.2 Optimal Sequential Policies; 4.2.3 Heuristic Policies; 4.3 Evaluating Policies; 4.4 More Advanced Topics*; 4.4.1 An Alternative Representation of the Probability Space; 4.4.2 Equivalence of Using True Means and Sample Estimates; 4.5 Bibliographic Notes; Problems; 5 The Knowledge Gradient 5.1 The Knowledge Gradient for Independent Beliefs5.1.1 Computation; 5.1.2 Some Properties of the Knowledge Gradient; 5.1.3 The Four Distributions of Learning; 5.2 The Value of Information and the S-Curve Effect; 5.3 Knowledge Gradient for Correlated Beliefs; 5.4 Anticipatory Versus Experiential Learning; 5.5 The Knowledge Gradient for Some Non-Gaussian Distributions; 5.5.1 The Gamma-Exponential Model; 5.5.2 The Gamma-Poisson Model; 5.5.3 The Pareto-Uniform Model; 5.5.4 The Beta-Bernoulli Model; 5.5.5 Discussion; 5.6 Relatives of the Knowledge Gradient; 5.6.1 Expected Improvement 5.6.2 Linear Loss*5.7 The Problem of Priors; 5.8 Discussion; 5.9 Why Does It Work?*; 5.9.1 Derivation of the Knowledge Gradient Formula; 5.10 Bibliographic Notes; Problems; 6 Bandit Problems; 6.1 The Theory and Practice of Gittins Indices; 6.1.1 Gittins Indices in the Beta-Bernoulli Model; 6.1.2 Gittins Indices in the Normal-Normal Model; 6.1.3 Approximating Gittins Indices; 6.2 Variations of Bandit Problems; 6.3 Upper Confidence Bounding; 6.4 The Knowledge Gradient for Bandit Problems; 6.4.1 The Basic Idea; 6.4.2 Some Experimental Comparisons; 6.4.3 Non-Normal Models; 6.5 Bibliographic Notes Problems |
| Record Nr. | UNINA-9910141254603321 |
|
Powell Warren B. <1955->
|
||
| Hoboken, NJ, : Wiley, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimal learning [[electronic resource] /] / Warren B. Powell, Ilya O. Ryzhov
| Optimal learning [[electronic resource] /] / Warren B. Powell, Ilya O. Ryzhov |
| Autore | Powell Warren B. <1955-> |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, 2012 |
| Descrizione fisica | 1 online resource (416 p.) |
| Disciplina | 006.3/1 |
| Altri autori (Persone) | RyzhovIlya Olegovich <1985-> |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Machine learning
Artificial intelligence |
| ISBN |
1-118-30984-7
1-280-59249-4 9786613622327 1-118-30453-5 1-118-30982-0 1-118-30985-5 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Optimal Learning; CONTENTS; Preface; Acknowledgments; 1 The Challenges of Learning; 1.1 Learning the Best Path; 1.2 Areas of Application; 1.3 Major Problem Classes; 1.4 The Different Types of Learning; 1.5 Learning from Different Communities; 1.6 Information Collection Using Decision Trees; 1.6.1 A Basic Decision Tree; 1.6.2 Decision Tree for Offline Learning; 1.6.3 Decision Tree for Online Learning; 1.6.4 Discussion; 1.7 Website and Downloadable Software; 1.8 Goals of this Book; Problems; 2 Adaptive Learning; 2.1 The Frequentist View; 2.2 The Bayesian View
2.2.1 The Updating Equations for Independent Beliefs2.2.2 The Expected Value of Information; 2.2.3 Updating for Correlated Normal Priors; 2.2.4 Bayesian Updating with an Uninformative Prior; 2.3 Updating for Non-Gaussian Priors; 2.3.1 The Gamma-Exponential Model; 2.3.2 The Gamma-Poisson Model; 2.3.3 The Pareto-Uniform Model; 2.3.4 Models for Learning Probabilities*; 2.3.5 Learning an Unknown Variance*; 2.4 Monte Carlo Simulation; 2.5 Why Does It Work?*; 2.5.1 Derivation of σ; 2.5.2 Derivation of Bayesian Updating Equations for Independent Beliefs; 2.6 Bibliographic Notes; Problems 3 The Economics of Information3.1 An Elementary Information Problem; 3.2 The Marginal Value of Information; 3.3 An information Acquisition Problem; 3.4 Bibliographic Notes; Problems; 4 Ranking and Selection; 4.1 The Model; 4.2 Measurement Policies; 4.2.1 Deterministic Versus Sequential Policies; 4.2.2 Optimal Sequential Policies; 4.2.3 Heuristic Policies; 4.3 Evaluating Policies; 4.4 More Advanced Topics*; 4.4.1 An Alternative Representation of the Probability Space; 4.4.2 Equivalence of Using True Means and Sample Estimates; 4.5 Bibliographic Notes; Problems; 5 The Knowledge Gradient 5.1 The Knowledge Gradient for Independent Beliefs5.1.1 Computation; 5.1.2 Some Properties of the Knowledge Gradient; 5.1.3 The Four Distributions of Learning; 5.2 The Value of Information and the S-Curve Effect; 5.3 Knowledge Gradient for Correlated Beliefs; 5.4 Anticipatory Versus Experiential Learning; 5.5 The Knowledge Gradient for Some Non-Gaussian Distributions; 5.5.1 The Gamma-Exponential Model; 5.5.2 The Gamma-Poisson Model; 5.5.3 The Pareto-Uniform Model; 5.5.4 The Beta-Bernoulli Model; 5.5.5 Discussion; 5.6 Relatives of the Knowledge Gradient; 5.6.1 Expected Improvement 5.6.2 Linear Loss*5.7 The Problem of Priors; 5.8 Discussion; 5.9 Why Does It Work?*; 5.9.1 Derivation of the Knowledge Gradient Formula; 5.10 Bibliographic Notes; Problems; 6 Bandit Problems; 6.1 The Theory and Practice of Gittins Indices; 6.1.1 Gittins Indices in the Beta-Bernoulli Model; 6.1.2 Gittins Indices in the Normal-Normal Model; 6.1.3 Approximating Gittins Indices; 6.2 Variations of Bandit Problems; 6.3 Upper Confidence Bounding; 6.4 The Knowledge Gradient for Bandit Problems; 6.4.1 The Basic Idea; 6.4.2 Some Experimental Comparisons; 6.4.3 Non-Normal Models; 6.5 Bibliographic Notes Problems |
| Record Nr. | UNINA-9910810636103321 |
|
Powell Warren B. <1955->
|
||
| Hoboken, NJ, : Wiley, 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Reinforcement learning and stochastic optimization : a unified framework for sequential decisions / / Warren B Powell
| Reinforcement learning and stochastic optimization : a unified framework for sequential decisions / / Warren B Powell |
| Autore | Powell Warren B. <1955-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc, , [2022] |
| Descrizione fisica | 1 online resource (1138 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Decision making - Statistical methods
Stochastic analysis Reinforcement learning Mathematical optimization |
| ISBN |
1-119-81506-1
1-119-81504-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910566695003321 |
|
Powell Warren B. <1955->
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Reinforcement learning and stochastic optimization : a unified framework for sequential decisions / / Warren B Powell
| Reinforcement learning and stochastic optimization : a unified framework for sequential decisions / / Warren B Powell |
| Autore | Powell Warren B. <1955-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc, , [2022] |
| Descrizione fisica | 1 online resource (1138 pages) |
| Disciplina | 006.31 |
| Soggetto topico |
Decision making - Statistical methods
Stochastic analysis Reinforcement learning Mathematical optimization |
| ISBN |
1-119-81506-1
1-119-81504-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910676639403321 |
|
Powell Warren B. <1955->
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||