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...
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...
Addison-Wesley Professional, 2019. — 416 p. — ISBN: 978-0135172384. The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice 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...
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...
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,...
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...
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. — ISBN13: 978-1-78883-652-4. A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python 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...
Packt Publishing, 2018. — 453 p. — ISBN: 978-1-78899-161-2. Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries 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,...
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...
O’Reilly Media, Inc., 2021. — 408 p. — ISBN: 978-1-098-11483-1. 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...
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...
Комментарии