Название: Cognitive Analytics and Reinforcement Learning: Theories, Techniques and Applications
Автор: Еlkkiуа R., Subrаmаniуаswаmу V.
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 384
Язык: английский
Формат: epub
Размер: 10.1 MB

The combination of cognitive analytics and Reinforcement Learning is a transformational force in the field of modern technological breakthroughs, reshaping the decision-making, problem-solving, and innovation landscape; this book offers an examination of the profound overlap between these two fields and illuminates its significant consequences for business, academia, and research. Cognitive analytics and Reinforcement Learning are pivotal branches of Artificial Intelligence. They have garnered increased attention in the research field and industry domain on how humans perceive, interpret, and respond to information. Cognitive science allows us to understand data, mimic human cognitive processes, and make informed decisions to identify patterns and adapt to dynamic situations. The process enhances the capabilities of various applications. Readers will uncover the latest advancements in AI and Machine Learning, gaining valuable insights into how these technologies are revolutionizing various industries, including transforming healthcare by enabling smarter diagnosis and treatment decisions, enhancing the efficiency of smart cities through dynamic decision control, optimizing debt collection strategies, predicting optimal moves in complex scenarios like chess, and much more. The book caters to a diverse audience that spans academic researchers, AI practitioners, data scientists, industry leaders, tech enthusiasts, and educators who associate with Artificial Intelligence, data analytics, and cognitive sciences.

Название: Reinforcement Learning for Finance: A Python-Based Introduction (Early Release)
Автор: Yvеs J. Нilрisсh
Издательство: O’Reilly Media, Inc.
Год: 2024-03-27
Страниц: 153
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Reinforcement Learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research. This book is among the first to explore the use of Reinforcement Learning methods in finance. Author Yves Hilpisch, founder and CEO of The Python Quants, provides the background you need in concise fashion. ML practitioners, financial traders, portfolio managers, strategists, and analysts will focus on the implementation of these algorithms in the form of self-contained Python code and the application to important financial problems. “Bayesian Learning” discusses Bayesian learning as an example of learning through interaction. “Reinforcement Learning” presents breakthroughs in artificial intelligence that were made possible through reinforcement learning. It also describes the major building blocks of reinforcement learning. “Deep Q-Learning” explains the two major characteristics of deep Q-learning which is the most important algorithm for the remainder of the book.

Название: The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python
Автор: Мiсhаеl Нu
Издательство: Apress
Год: 2024
Страниц: 290
Язык: английский
Формат: pdf (true), epub (true)
Размер: 24.3 MB

Unlock the full potential of Reinforcement Learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RL's core concepts, mathematics, and practical algorithms, helping you to develop a thorough understanding of this cutting-edge technology. Beginning with an overview of fundamental concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, and temporal difference learning, this book uses clear and concise examples to explain the basics of RL theory. The following section covers value function approximation, a critical technique in RL, and explores various policy approximations such as policy gradient methods and advanced algorithms like Proximal Policy Optimization (PPO). This book also delves into advanced topics, including distributed reinforcement learning, curiosity-driven exploration, and the famous AlphaZero algorithm, providing readers with a detailed account of these cutting-edge techniques. For Machine Learning engineers, data scientists, software engineers, and developers who want to incorporate reinforcement learning algorithms into their projects and applications.

Название: Грокаем глубокое обучение с подкреплением
Автор: Mигeль Mopaлec
Издательство: Питер
Год: 2023
Страниц: 464
Язык: русский
Формат: pdf
Размер: 22.9 MB

Мы учимся, взаимодействуя с окружающей средой, и получаемые вознаграждения и наказания определяют наше поведение в будущем. Глубокое обучение с подкреплением (reinforcement learning, RL) привносит этот естественный процесс в искусственный интеллект и предполагает анализ результатов для выявления наиболее эффективных путей движения вперед. Агенты глубокого обучения с подкреплением могут способствовать успеху маркетинговых кампаний, прогнозировать рост акций и побеждать гроссмейстеров в Го и шахматах. Давайте научимся создавать системы глубокого обучения на примере увлекательных упражнений, сопровождаемых кодом на Python с подробными комментариями и понятными объяснениями. Вы увидите, как работают алгоритмы, и научитесь создавать собственных агентов глубокого обучения с подкреплением, используя оценочную обратную связь.

Название: Reinforcement Learning Explained - A Step-by-Step Guide to Reward-Driven AI
Автор: Luka Nikolic
Издательство: Leanpub
Год: 2023-08-08
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

Discover the thrilling world of Reinforcement Learning (RL) in our engaging eBook! Learn the fundamentals of RL, from AI agents and environments to rewards and actions. Explore real-world applications like robotics, healthcare, and personalized recommendations, where RL is transforming industries. Reinforcement Learning is a type of Machine Learning where a computer program (the agent) learns to make better decisions by trying different actions and getting rewards for good choices. Stay ahead with the latest trends and cutting-edge research in RL. Collaborate with like-minded enthusiasts and contribute to the dynamic field of RL, driving advancements that shape the future of AI. Whether you're a beginner or AI enthusiast, this eBook will fuel your passion for RL and its limitless possibilities.

Название: Fundamentals of Reinforcement Learning
Автор: Rafael Ris-Ala
Издательство: Springer
Год: 2023
Страниц: 103
Язык: английский
Формат: pdf (true), epub
Размер: 18.8 MB

Artificial Intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization. This book provides an introduction to AI, specifies Machine Learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner. It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges. Some main libraries and frameworks are available for implementing RL algorithms, such as TensorFlow, Keras, and OpenAI Gym. Some useful data sources for conducting your RL experiments are also discussed. Practice with commented Python code.

Название: Adaptive and Learning-Based Control of Safety-Critical Systems
Автор: Max Cohen, Calin Belta
Издательство: Springer
Серия: Synthesis Lectures on Computer Science
Год: 2023
Страниц: 209
Язык: английский
Формат: pdf (true), epub
Размер: 21.5 MB

Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) have facilitated the design of control and decision-making policies directly from data. For example, advancements in Reinforcement Learning (RL) have enabled robots to learn high-performance control policies purely from trial-and-error interaction with their environment. This book stems from the growing use of learning-based techniques, such as Reinforcement Learning and adaptive control, in the control of autonomous and safety-critical systems. Safety is critical to many applications, such as autonomous driving, air traffic control, and robotics. As these learning-enabled technologies become more prevalent in the control of autonomous systems, it becomes increasingly important to ensure that such systems are safe. To address these challenges, the authors provide a self-contained treatment of learning-based control techniques with rigorous guarantees of stability and safety. This book contains recent results on provably correct control techniques from specifications that go beyond safety and stability, such as temporal logic formulas. The authors bring together control theory, optimization, Machine Learning, and formal methods and present worked-out examples and extensive simulation examples to complement the mathematical style of presentation. Prerequisites are minimal, and the underlying ideas are accessible to readers with only a brief background in control-theoretic ideas, such as Lyapunov stability theory.

Название: Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
Автор: Agostino Capponi, Charles-Albert Lehalle
Издательство: Cambridge University Press
Год: 2023
Страниц: 741
Язык: английский
Формат: pdf
Размер: 66.3 MB

Machine learning, Artificial Intelligence (AI), and Data Science (DS) pervade every aspect of our everyday life. Many of the techniques developed by the Computer Science community are becoming increasingly used in the area of financial engineering, ranging from the use of Deep Learning methods for hedging and risk management through the exploitation of AI techniques for investment or design of trading systems. These techniques are also having enormous implications on the operations of financial markets. Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in Machine Learning for financial markets. Instead of seeing Machine Learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by Data Sciences and Artificial Intelligence.

Название: Алгоритмы принятия решений
Автор: Koxeндepфep M., Уилep T., Pэй K.
Издательство: ДМК Пресс
Год: 2023
Страниц: 686
Язык: русский
Формат: pdf
Размер: 21.9 MB

Системы автоматического принятия решений и поддержки принятия решений человеком широко используются в различных областях – от предотвращения столкновений самолетов до скрининга рака молочной железы. При разработке таких систем важно учитывать различные источники неопределенности, тщательно соблюдая баланс между несколькими целями. Данная книга представляет собой полное введение в теорию алгоритмов принятия решений в условиях неопределенности, включая формулировки основных математических задач и методы их решения. Основное внимание уделяется планированию и обучению с подкреплением (англ. reinforcement learning), хотя некоторые из представленных методов основаны на элементах обучения с учителем (англ. supervised learning) и оптимизации. В основе этого учебника лежат алгоритмы, реализованные на языке программирования Julia. Мы обнаружили, что язык Julia идеально подходит для описания алгоритмов в удобочитаемой форме.

Название: Differential Evolution: From Theory to Practice
Автор: B. Vinoth K., Diego Oliva, P. N. Suganthan
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2022
Страниц: 389
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. Differential evolution is a population-based meta-heuristic technique for global optimization capable of handling non-differentiable, non-linear and multi-modal objective functions. Many advances have been made recently in differential evolution, from theory to applications. This book comprises contributions which include theoretical developments in DE, performance comparisons of DE, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications. The book is useful for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research and natural computing.