Reinforcement learning and optimal control, Bertsekas, Dimitri P.
Автор: Sutton Richard S., Barto Andrew G. Название: Reinforcement Learning: An Introduction, 2 ed. ISBN: 0262039249 ISBN-13(EAN): 9780262039246 Издательство: MIT Press Рейтинг: Цена: 18850.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.
Like the first edition, this second edition focuses on core, online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new for the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Автор: Mindess, Sidney Название: Developments in the Formulation and Reinforcement of Concrete ISBN: 0081026161 ISBN-13(EAN): 9780081026168 Издательство: Elsevier Science Рейтинг: Цена: 38739.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
In the first edition of this book, Developments in the Formulation and Reinforcement of Concrete, a number of what are often referred to as "specialty" concretes were discussed. These concretes through specialty mix design offer high physical properties and enhanced performance. The information discussed in these chapters is still relevant today, but in the ten years since the first edition was published, a number of other key areas have become increasingly important in modern concrete technology. There is now much more emphasis on sustainability within the cement and concrete industries. This requires a greater emphasis and understanding of some of the major durability problems that the scientist or engineer may encounter, such as chloride corrosion of steel, and alkali-aggregate reactions. There is also increasing use of specifications involving explicit service life requirements (100 years being common), but this is a concept that is still widely misunderstood.
In this second edition of the book all material previously covered on specialty concretes is brought fully up-to-date taking into consideration latest developments and with the addition of new chapters on supplementary cementitious materials; mass concrete; the sustainably of concrete; service life prediction; limestone cements; corrosion of steel in concrete; alkali-aggregate reactions and concrete as a multiscale material the chapters will introduce the reader to some of the most important issues facing the concrete industry today. In keeping with the previous edition, the international team of contributors, are all at the cutting edge in their own areas of research and have wide industrial experience.
With its distinguished editor and international team of contributors, Developments in the Formulation and Reinforcement of Concrete, Second Edition is a standard reference for civil and structural engineers.
Summarizes a wealth of recent research on structural concrete including material microstructure, concrete types, variation and construction techniques
Emphasizes concrete mixture design and applications in civil and structural engineering
Reviews modern concrete materials to novel construction systems, such as the precast industry and structures requiring high-performance concrete
Автор: Kyriakos G. Vamvoudakis, Nick-Marios T. Kokolakis Название: Synchronous Reinforcement Learning-Based Control for Cognitive Autonomy ISBN: 1680837443 ISBN-13(EAN): 9781680837445 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12197.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Describes the use of principles of reinforcement learning (RL) to design feedback policies for continuous-time dynamical systems that combine features of adaptive control and optimal control. The authors give an insightful introduction to reinforcement learning techniques that can address various control problems.
Автор: Vamvoudakis Kyriakos G., Wan Yan, Lewis Frank L. Название: Handbook of Reinforcement Learning and Control ISBN: 3030609898 ISBN-13(EAN): 9783030609894 Издательство: Springer Цена: 32142.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Cognitive Dialogue: A New Architecture for Perception and Cognition.- Rooftop-Aware Emergency Landing Planning for Small Unmanned Aircraft Systems.- Quantum Reinforcement Learning in Changing Environment.- The Role of Thermodynamics in the Future Research Directions in Control and Learning.- Mixed Density Reinforcement Learning Methods for Approximate Dynamic Programming.- Analyzing and Mitigating Link-Flooding DoS Attacks Using Stackelberg Games and Adaptive Learning.- Learning and Decision Making for Complex Systems Subjected to Uncertainties: A Stochastic Distribution Control Approach.- Optimal Adaptive Control of Partially Unknown Linear Continuous-time Systems with Input and State Delay.- Gradient Methods Solve the Linear Quadratic Regulator Problem Exponentially Fast.- Architectures, Data Representations and Learning Algorithms: New Directions at the Confluence of Control and Learning.- Reinforcement Learning for Optimal Feedback Control and Multiplayer Games.- Fundamental Principles of Design for Reinforcement Learning Algorithms Course Titles.- Long-Term Impacts of Fair Machine Learning.- Learning-based Model Reduction for Partial Differential Equations with Applications to Thermo-Fluid Models' Identification, State Estimation, and Stabilization.- CESMA: Centralized Expert Supervises Multi-Agents, for Decentralization.- A Unified Framework for Reinforcement Learning and Sequential Decision Analytics.- Trading Utility and Uncertainty: Applying the Value of Information to Resolve the Exploration-Exploitation Dilemma in Reinforcement Learning.- Multi-Agent Reinforcement Learning: Recent Advances, Challenges, and Applications.- Reinforcement Learning Applications, An Industrial Perspective.- A Hybrid Dynamical Systems Perspective of Reinforcement Learning.- Bounded Rationality and Computability Issues in Learning, Perception, Decision-Making, and Games Panagiotis Tsiotras.- Mixed Modality Learning.- Computational Intelligence in Uncertainty Quantification for Learning Control and Games.- Reinforcement Learning Based Optimal Stabilization of Unknown Time Delay Systems Using State and Output Feedback.- Robust Autonomous Driving with Humans in the Loop.- Boundedly Rational Reinforcement Learning for Secure Control.
Описание: With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning`s core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intelligent applications with ease.
Автор: Richard S. Sutton Название: Reinforcement Learning ISBN: 0792392345 ISBN-13(EAN): 9780792392347 Издательство: Springer Рейтинг: Цена: 30606.00 р. 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: Sugiyama Название: Statistical Reinforcement Learning ISBN: 1439856893 ISBN-13(EAN): 9781439856895 Издательство: Taylor&Francis Рейтинг: Цена: 13014.00 р. 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.
Описание: This book is a practical, developer-oriented introduction to deep reinforcement learning (RL). Explore the theoretical concepts of RL, before discovering how deep learning (DL) methods and tools are making it possible to solve more complex and challenging problems than ever before. Apply deep RL methods to training your agent to beat arcade ...
Описание: Shows how to avoid or correct typical behaviour problems, including jumping, barking, and lead-pulling. This title covers hand-feeding; crate and potty training; and basic cues - sit, stay, come here - as well as complex goals, such as bite inhibition and water safety.
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles.
Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application.
In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles.
Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application.
In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
Автор: Christopher Gatti Название: Design of Experiments for Reinforcement Learning ISBN: 3319385518 ISBN-13(EAN): 9783319385518 Издательство: Springer Рейтинг: Цена: 15372.00 р. 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.
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