Reinforcement Learning And Stochastic Optimization
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Author |
: Warren B. Powell |
Publisher |
: John Wiley & Sons |
Total Pages |
: 1090 |
Release |
: 2022-04-25 |
ISBN-10 |
: 9781119815051 |
ISBN-13 |
: 1119815053 |
Rating |
: 4/5 (51 Downloads) |
Synopsis Reinforcement Learning and Stochastic Optimization by : Warren B. Powell
REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a "diary problem" that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.
Author |
: Vinod Kumar Chauhan |
Publisher |
: CRC Press |
Total Pages |
: 189 |
Release |
: 2021-11-18 |
ISBN-10 |
: 9781000505610 |
ISBN-13 |
: 1000505618 |
Rating |
: 4/5 (10 Downloads) |
Synopsis Stochastic Optimization for Large-scale Machine Learning by : Vinod Kumar Chauhan
Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.
Author |
: Deniz Daniel Preil |
Publisher |
: |
Total Pages |
: 0 |
Release |
: 2022 |
ISBN-10 |
: OCLC:1349545720 |
ISBN-13 |
: |
Rating |
: 4/5 (20 Downloads) |
Synopsis Reinforcement Learning for Discrete Stochastic Optimization by : Deniz Daniel Preil
Author |
: Vinod Kumar Chauhan |
Publisher |
: CRC Press |
Total Pages |
: 177 |
Release |
: 2021-11-18 |
ISBN-10 |
: 9781000505535 |
ISBN-13 |
: 1000505537 |
Rating |
: 4/5 (35 Downloads) |
Synopsis Stochastic Optimization for Large-scale Machine Learning by : Vinod Kumar Chauhan
Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.
Author |
: Guanghui Lan |
Publisher |
: Springer Nature |
Total Pages |
: 591 |
Release |
: 2020-05-15 |
ISBN-10 |
: 9783030395681 |
ISBN-13 |
: 3030395685 |
Rating |
: 4/5 (81 Downloads) |
Synopsis First-order and Stochastic Optimization Methods for Machine Learning by : Guanghui Lan
This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
Author |
: R. Cairoli |
Publisher |
: John Wiley & Sons |
Total Pages |
: 348 |
Release |
: 2011-07-26 |
ISBN-10 |
: 9781118164402 |
ISBN-13 |
: 1118164407 |
Rating |
: 4/5 (02 Downloads) |
Synopsis Sequential Stochastic Optimization by : R. Cairoli
Sequential Stochastic Optimization provides mathematicians andapplied researchers with a well-developed framework in whichstochastic optimization problems can be formulated and solved.Offering much material that is either new or has never beforeappeared in book form, it lucidly presents a unified theory ofoptimal stopping and optimal sequential control of stochasticprocesses. This book has been carefully organized so that littleprior knowledge of the subject is assumed; its only prerequisitesare a standard graduate course in probability theory and somefamiliarity with discrete-parameter martingales. Major topics covered in Sequential Stochastic Optimization include: * Fundamental notions, such as essential supremum, stopping points,accessibility, martingales and supermartingales indexed by INd * Conditions which ensure the integrability of certain suprema ofpartial sums of arrays of independent random variables * The general theory of optimal stopping for processes indexed byInd * Structural properties of information flows * Sequential sampling and the theory of optimal sequential control * Multi-armed bandits, Markov chains and optimal switching betweenrandom walks
Author |
: Yi Zhou |
Publisher |
: |
Total Pages |
: 0 |
Release |
: 2024-07-11 |
ISBN-10 |
: 1638283702 |
ISBN-13 |
: 9781638283706 |
Rating |
: 4/5 (02 Downloads) |
Synopsis Stochastic Optimization Methods for Policy Evaluation in Reinforcement Learning by : Yi Zhou
This monograph introduces various value-based approaches for solving the policy evaluation problem in the online reinforcement learning (RL) scenario, which aims to learn the value function associated with a specific policy under a single Markov decision process (MDP). Approaches vary depending on whether they are implemented in an on-policy or off-policy manner. In on-policy settings, where the evaluation of the policy is conducted using data generated from the same policy that is being assessed, classical techniques such as TD(0), TD(λ), and their extensions with function approximation or variance reduction are employed in this setting. For off-policy evaluation, where samples are collected under a different behavior policy, this monograph introduces gradient-based two-timescale algorithms like GTD2, TDC, and variance-reduced TDC. These algorithms are designed to minimize the mean-squared projected Bellman error (MSPBE) as the objective function. This monograph also discusses their finite-sample convergence upper bounds and sample complexity.
Author |
: James C. Spall |
Publisher |
: John Wiley & Sons |
Total Pages |
: 620 |
Release |
: 2005-03-11 |
ISBN-10 |
: 9780471441908 |
ISBN-13 |
: 0471441902 |
Rating |
: 4/5 (08 Downloads) |
Synopsis Introduction to Stochastic Search and Optimization by : James C. Spall
* Unique in its survey of the range of topics. * Contains a strong, interdisciplinary format that will appeal to both students and researchers. * Features exercises and web links to software and data sets.
Author |
: John R. Birge |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 427 |
Release |
: 2006-04-06 |
ISBN-10 |
: 9780387226187 |
ISBN-13 |
: 0387226184 |
Rating |
: 4/5 (87 Downloads) |
Synopsis Introduction to Stochastic Programming by : John R. Birge
This rapidly developing field encompasses many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors present a broad overview of the main themes and methods of the subject, thus helping students develop an intuition for how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. The early chapters introduce some worked examples of stochastic programming, demonstrate how a stochastic model is formally built, develop the properties of stochastic programs and the basic solution techniques used to solve them. The book then goes on to cover approximation and sampling techniques and is rounded off by an in-depth case study. A well-paced and wide-ranging introduction to this subject.
Author |
: Csaba Grossi |
Publisher |
: Springer Nature |
Total Pages |
: 89 |
Release |
: 2022-05-31 |
ISBN-10 |
: 9783031015519 |
ISBN-13 |
: 3031015517 |
Rating |
: 4/5 (19 Downloads) |
Synopsis Algorithms for Reinforcement Learning by : Csaba Grossi
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration