Apress Media, LLC., 2023. — 435 p. — ISBN-13: 978-1-4842-8835-1. This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library. Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN – two kinds of neural networks used as...
The MIT Press, 2023. — 379 p. — (Adaptive Computation and Machine Learning). The first comprehensive guide to Distributional Reinforcement Learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional Reinforcement Learning is a new mathematical formalism for thinking about decisions. Going beyond the common...
Springer, 2021. — 214 p. — ISBN 978-3-030-41187-9. This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms,...
Packt Publishing, 2021. — 484 p.— ISBN 1838644148, 9781838644147. Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices Key Features - Understand how large-scale state-of-the-art RL algorithms and approaches work -...
Packt Publishing, 2019. — 362 p. — ISBN: 1789616719, 9781789616712. Implement key reinforcement learning algorithms and techniques using different R packages such as the Markov chain, MDP toolbox, contextual, and OpenAI Gym Key Features Explore the design principles of reinforcement learning and deep reinforcement learning models Use dynamic programming to solve design issues...
Springer, 2020. — 541 p. — ISBN: 9811540942 Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in...
Addison-Wesley Professional, 2019. — 655 p. Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics.
BPB Online, 2022. — 526 p. — ISBN 978-93-55512-055. Introducing Practical Smart Agents Development using Python, PyTorch, and TensorFlow Key Features ● Exposure to well-known RL techniques, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical. ● Hands-on experience with TensorFlow and PyTorch on Reinforcement Learning projects. ● Everything is concise,...
O’Reilly Media, Inc., 2025. — 236 р. — ISBN-13: 978-1-098-16914-5. 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...
Wiley, 2023. — 288 p. The book covers diverse applications of DRL to address various problems in wireless networks, such as caching, offloading, resource sharing, and security. The authors discuss open issues by introducing some advanced DRL approaches to address emerging issues in wireless communications and networking. Covering new advanced models of DRL, e.g., deep dueling...
Packt Publishing, 2018. — 546 p. — ISBN: 978-1788834247. This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO,...
Packt Publishing, 2019. — 356 p. — ISBN: 978-1-78913-111-6. Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will...
John Wiley & Sons, Inc., 2025. — 416 p. — ISBN 978-1-394-27255-6. Глубокое обучение с подкреплением и примеры его промышленного использования: искусственный интеллект для реальных приложений This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts,...
Apress, 2020. — 564 p. — ISBN 1484265025. This book starts with an introduction to state-based reinforcement learning algorithms involving Markov models, Bellman equations, and writing custom C# code with the aim of contrasting value and policy-based functions in reinforcement learning. Then, you will move on to path finding and navigation meshes in Unity, setting up the ML...
Manning Publications, 2020. — 472 p. — ISBN 978-1617295454. We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the...
Manning Publications, 2020. — 505 p. — ISBN: 978-1617295454. Grokking Deep Reinforcement Learning is a beautifully balanced approach to teaching, offering numerous large and small examples, annotated diagrams and code, engaging exercises, and skillfully crafted writing. You'll explore, discover, and learn as you lock in the ins and outs of reinforcement learning, neural...
Apress, 2018. — 174 p. Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process. Reinforcement...
Packt Publishing, 2021. — 472 p. — ISBN 9781838982546. Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning Key Features Develop and deploy deep reinforcement learning-based solutions to production pipelines, products, and services Explore popular reinforcement learning algorithms such as Q-learning,...
CRC Press, 2023. — 522 p. — ISBN: 978-1-003-22919-3. Reinforcement Learning (RL) is emerging as a practical, powerful technique for solving a variety of complex business problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Although RL is classified as a branch of Machine Learning (ML), it tends to be viewed and treated quite differently from...
Packt, 2018. — 318 p. Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI...
Springer, 2023. — 96 p. — ISBN 3031373448. 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...
Packt, 2018. — 296 p. Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years. In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning...
2nd Edition. — Apress Media LLC, 2024. — 659 p. — ISBN-13: 979-8-8688-0273-7. Gain a theoretical understanding to the most popular libraries in deep reinforcement learning (deep RL). This new edition focuses on the latest advances in deep RL using a learn-by-coding approach, allowing readers to assimilate and replicate the latest research in this field. New agent environments...
Springer, 2019. — 214 p. — ISBN: 9811382840. This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book...
Springer, 2021. — 839 p. — ISBN 978-3-030-60989-4. This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to...
O’Reilly, 2020. — 408 p. — ISBN: 1098114833. Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI...
Independent publication, 2024. — 224 р. In a world where machines are constantly pushing the boundaries of human capabilities, one field stands out as a beacon of innovation: Reinforcement Learning (RL). In "Reinforcement Learning: Amplifying AI - Shattering Boundaries with Reinforcement Learning," embark on an exhilarating journey into the heart of AI's next frontier....
Manning Publications, 2020. — 367 p. — ISBN: 978-1617295430. Deep Reinforcement Learning in Action teaches you how to program agents that learn and improve based on direct feedback from their environment. You’ll build networks with the popular PyTorch deep learning framework to explore reinforcement learning algorithms ranging from Deep Q-Networks to Policy Gradients methods to...
Пер. с англ. Волошко Р. — СПб.: Питер, 2023. — 464 с.: ил. — (Библиотека программиста). — ISBN: 978-5-4461-3944-6. Мы учимся, взаимодействуя с окружающей средой, и получаемые вознаграждения и наказания определяют наше поведение в будущем. Глубокое обучение с подкреплением привносит этот естественный процесс в искусственный интеллект и предполагает анализ результатов для...
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