Recent Advances in Reinforcement Learning, Leslie Pack Kaelbling
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.
Название: Recent advances in reinforcement learning ISBN: 0792397053 ISBN-13(EAN): 9780792397052 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Addresses research in the Artificial Intelligence and Neural Network communities. This book includes topics such as the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques.
Автор: Sertan Girgin; Manuel Loth; R?mi Munos; Philippe P Название: Recent Advances in Reinforcement Learning ISBN: 3540897216 ISBN-13(EAN): 9783540897217 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Constitutes the revised and selected papers of the 8th European Workshop on Reinforcement Learning, EWRL 2008, which took place in Villeneuve d`Ascq, France, during June 30 - July 3, 2008.
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: 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: 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: Shimon Whiteson Название: Adaptive Representations for Reinforcement Learning ISBN: 3642422314 ISBN-13(EAN): 9783642422317 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Presenting the main results of new algorithms for reinforcement learning, this book also introduces a novel method for devising input representations as well as presenting a way to find a minimal set of features sufficient to describe the agent`s current state.
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: 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: Matthew Taylor Название: Transfer in Reinforcement Learning Domains ISBN: 3642018815 ISBN-13(EAN): 9783642018817 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Reinforcement Learning Background.- Related Work.- Empirical Domains.- Value Function Transfer via Inter-Task Mappings.- Extending Transfer via Inter-Task Mappings.- Transfer between Different Reinforcement Learning Methods.- Learning Inter-Task Mappings.- Conclusion and Future Work.
Автор: Matthew Taylor Название: Transfer in Reinforcement Learning Domains ISBN: 3642101860 ISBN-13(EAN): 9783642101861 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research.
Автор: Verena Rieser; Oliver Lemon Название: Reinforcement Learning for Adaptive Dialogue Systems ISBN: 3642439845 ISBN-13(EAN): 9783642439841 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book contributes to progress in spoken dialogue systems with a new, data-driven methodology. Covers Spoken and Multimodal dialogue systems; Wizard-of-Oz data collection; User Simulation methods; Reinforcement Learning and Evaluation methodologies.
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