Автор: Kathryn E. Merrick; Mary Lou Maher Название: Motivated Reinforcement Learning ISBN: 364210035X ISBN-13(EAN): 9783642100352 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book covers the design, application and evaluation of computational models of motivation in reinforcement learning. The performance of these models is demonstrated by applications in simulated game scenarios and a live, open-ended, virtual world.
Автор: Rieser, Verena Название: Reinforcement Learning for Adaptive Dialogue Systems ISBN: 3642249418 ISBN-13(EAN): 9783642249419 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The past decade has seen a revolution in the field of spoken dialogue systems. As in other areas of Computer Science and Artificial Intelligence, data-driven methods are now being used to drive new methodologies for system development and evaluation. This book is a unique contribution to that ongoing change. A new methodology for developing spoken dialogue systems is described in detail. The journey starts and ends with human behaviour in interaction, and explores methods for learning from the data, for building simulation environments for training and testing systems, and for evaluating the results. The detailed material covers: Spoken and Multimodal dialogue systems, Wizard-of-Oz data collection, User Simulation methods, Reinforcement Learning, and Evaluation methodologies. The book is a research guide for students and researchers with a background in Computer Science, AI, or Machine Learning. It navigates through a detailed case study in data-driven methods for development and evaluation of spoken dialogue systems. Common challenges associated with this approach are discussed and example solutions are provided. This work provides insights, lessons, and inspiration for future research and development – not only for spoken dialogue systems in particular, but for data-driven approaches to human-machine interaction in general.
Автор: Todd Hester Название: TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains ISBN: 3319375105 ISBN-13(EAN): 9783319375106 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.
Автор: Christopher Gatti Название: Design of Experiments for Reinforcement Learning ISBN: 3319385518 ISBN-13(EAN): 9783319385518 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge.
Автор: Richard S. Sutton Название: Reinforcement Learning ISBN: 0792392345 ISBN-13(EAN): 9780792392347 Издательство: Springer Рейтинг: Цена: 204040.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Reinforcement learning is the learning of mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take but instead must discover which actions yield the highest reward. This book contains research data on the subject.
Автор: Abhishek Nandy; Manisha Biswas Название: Reinforcement Learning ISBN: 1484232844 ISBN-13(EAN): 9781484232842 Издательство: Springer Рейтинг: Цена: 35390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Chapter 1: Reinforcement Learning basicsChapter Goal: This chapter covers the basics needed for AI, ML and Deep Learning.Relation between them and differences.No of pages 30Sub -Topics1. Reinforcement Learning2. The flow3. Faces of Reinforcement Learning4. 5. Environments6. The depiction of inter relation between Agents and EnvironmentDeep Learning Chapter 2: Theory and AlgorithmsChapter Goal: This Chapter covers the theory of Reinforcement Learning and Algorithms.No of pages: 60Sub-topics1 . Problem scenarios in Reinforcement Learningins 2. Markov Decision process3. SARSA4.Q learning5.Value Functions6.Dynamic Programming and Policies7.Approaches to RL Chapter 3: Open AI basicsChapter Goal: In this chapter we will cover the basics of Open AI gym and universe and then move forward for installing it. No of pages: 40 Sub - Topics: 1. What are Open AI environments 2. Installation of Open AI Gym and Universe in Ubuntu 3. Difference between Open AI Gym and Universe Chapter 4: Getting to know Open AI and Open AI gym the developers wayChapter Goal: We will use Python to start the programming and cover topics accordinglyNo of pages: 60Sub - Topics: 1. Open AI, Open AI Gym and python2. Setting up the environment3. Examples4 Swarm Intelligence using python 5.Markov Decision process toolbox for Python6.Implementing a Game AI with Reinforcement Learning Chapter 5: Reinforcement learning using Tensor Flow environment and KerasChapter Goal: We cover Reinforcement Learning in terms of Tensorflow and KerasNo of pages: 40Sub - Topics: 1. Tensorflow and Reinforcement Learning2. Q learning with Tensor Flow3. Keras4. Keras and Reinforcement Learning Chapter 6 Google's DeepMind and the future of Reinforcement LearningChapter Goal: We cover the descriptions of the above the content.No of pages: 25Sub - Topics: 1. Google's Deep Mind2. Future of Reinforcement Learning 3. Man VS Machines where is it Heading to.
Автор: Christopher Gatti Название: Design of Experiments for Reinforcement Learning ISBN: 3319121960 ISBN-13(EAN): 9783319121963 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: GLOSSARY ACKNOWLEDGMENT FOREWARD 1. INTRODUCTION 2. REINFORCEMENT LEARNING 2.1 Applications of reinforcement learning 2.1.1 Benchmark problems 2.1.2 Games 2.1.3 Real-world applications 2.1.4 Generalized domains 2.2 Components of reinforcement learning 2.2.1 Domains 2.2.2 Representations 2.2.3 Learning algorithms 2.3 Heuristics and performance effectors 3. DESIGN OF EXPERIMENTS 3.1 Classical design of experiments 3.2 Contemporary design of experiments 3.3 Design of experiments for empirical algorithm analysis 4. METHODOLOGY 4.1 Sequential CART 4.1.1 CART modeling 4.1.2 Sequential CART modeling 4.1.3 Analysis of sequential CART 4.1.4 Empirical convergence criteria 4.1.5 Example: 2-D 6-hump camelback function 4.2 Kriging metamodeling 4.2.1 Kriging 4.2.2 Deterministic kriging 4.2.3 Stochastic kriging 4.2.4 Covariance function 4.2.5 Implementation 4.2.6 Analysis of kriging metamodels 5. THE MOUNTAIN CAR PROBLEM 5.1 Reinforcement learning implementation 5.2 Sequential CART 5.3 Response surface metamodeling 5.4 Discussion 6. THE TRUCK BACKER-UPPER PROBLEM 6.1 Reinforcement learning implementation 6.2 Sequential CART 6.3 Response surface metamodeling 6.4 Discussion 7. THE TANDEM TRUCK BACKER-UPPER PROBLEM 7.1 Reinforcement learning implementation 7.2 Sequential CART 7.3 Discussion 8. DISCUSSION 8.1 Reinforcement learning 8.2 Experimentation 8.3 Innovations 8.4 Future work APPENDICES A. Parameter effects in the game of Chung Toi B. Design of experiments for the mountain car problem C. Supporting tables
Автор: Todd Hester Название: TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains ISBN: 3319011677 ISBN-13(EAN): 9783319011677 Издательство: Springer Рейтинг: Цена: 130610.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.
Автор: Leslie Pack Kaelbling Название: Recent Advances in Reinforcement Learning ISBN: 1441951601 ISBN-13(EAN): 9781441951601 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Sugiyama Название: Statistical Reinforcement Learning ISBN: 1439856893 ISBN-13(EAN): 9781439856895 Издательство: Taylor&Francis Рейтинг: Цена: 86760.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data.
Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods.
Covers the range of reinforcement learning algorithms from a modern perspective
Lays out the associated optimization problems for each reinforcement learning scenario covered
Provides thought-provoking statistical treatment of reinforcement learning algorithms
The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques.
This book is an ideal resource for graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields.
Автор: Sutton, Richard S. Barto, Andrew G. Название: Reinforcement learning ISBN: 0262193981 ISBN-13(EAN): 9780262193986 Издательство: MIT Press Рейтинг: Цена: 66930.00 T Наличие на складе: Нет в наличии. Описание: An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field`s intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.
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