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Handbook of Learning and Approximate Dynamic Programming

to start implementing the learning algorithms provided in the book* Includes ideas, directions, and recent results on current research issues;

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Approximate Dynamic Programming

Understanding approximate dynamic programming (ADP) in large industrial settings helps develop practical and high-quality solutions to;

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Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and;

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Approximate Dynamic Programming for Dynamic Vehicle Routing

researcher looking for effective and efficient methods of stochastic dynamic optimization and approximate dynamic programming (ADP). To this end, the;

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Dynamic and Stochastic Multi-Project Planning

This book deals with dynamic and stochastic methods for multi-project planning. Based on the idea of using queueing networks for the;

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Electric Power System Applications of Optimization

of Adaptive Critics Design and practical applications of approximate dynamic programming. To round out the coverage, the final chapter combines;

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Electric Power System Applications of Optimization

of Adaptive Critics Design and practical applications of approximate dynamic programming. To round out the coverage, the final chapter combines;

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Networked Control Systems with Intermittent Feedback

Dynamic Programming (ADP) and Reinforcement Learning (RL); and control systems with exogenous disturbances and modeling uncertainties.;

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Networked Control Systems with Intermittent Feedback

Dynamic Programming (ADP) and Reinforcement Learning (RL); and control systems with exogenous disturbances and modeling uncertainties.;

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SAGE Handbook of Play and Learning in Early Childhood

in play and learning scholarship, offering clarity and breadth in this dynamic and changing field.;

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Foundations of Probabilistic Programming

programming and their applications in machine learning, security, and other domains, at a level suitable for graduate students and non-experts in the;

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Handbook of Approximate Bayesian Computation

for intractable models for both experts and newcomers alike. It is an essential reference book for anyone interested in learning about and;

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Handbook of Reinforcement Learning and Control

when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single;

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Flexible Text Searching

implementations are based on the static and dynamic master-worker methods. Further, the authors propose a hybrid parallel implementation that combines the;

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Self-Learning Optimal Control of Nonlinear Systems

This book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which;

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Dynamic Web Programming

any online entitlements included with the product. Move beyond basic Web design by learning to create dynamic Web content To create a;

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Dynamic Programming

their long-term survival and benefit. To do this successfully means learning something about the dynamics of natural processes, and then using;

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Partially Observed Markov Decision Proce

of stochastic dynamic programming, lattice programming and reinforcement learning for POMDPs. Questions addressed in the book include: when does a POMDP;

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Stochastic Approximation

dynamic programming, and some models of collective behaviour. Three appendices give background on differential equations and probability.;

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Parallel Programming

reader with the understanding of parallel programing so as to analyze the differences in modular programming, recursive programming and dynamic;

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Dynamic Economics

An integrated approach to the empirical application of dynamic optimization programming models, for students and researchers. This book;

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Learning and Intelligent Optimization

optimization problems. Special focus is given to algorithm selection and configuration, learning, fitness landscape, applications, dynamic optimization;

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C++ for Engineers and Scientists

presentation of object-oriented concepts. Dynamic case studies, career spotlights and engineering-driven applications showcase the relevance;

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Introduction to Numerical Programming

programming skills. The book incorporates real code, and includes examples and problem sets to assist in hands-on learning. Begins with an;

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Handbook Of SAS DATA Step Programming

To write an accomplished program in the DATA step of SAS (R), programmers must understand programming logic and know how to implement and;

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Markov Decision Processes in Practice

methods such as policy improvement, successive approximation and infinite state spaces as well as an instructive chapter on Approximate Dynamic;

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Iterative Dynamic Programming

singular control problems. Recently, iterative dynamic programming (IDP) has been refined to handle inequality state constraints and noncontinuous;

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