The MIT Press, 2023. — 379 p. — (Adaptive Computation and Machine Learning). The first comprehensive guide to Distributional Reinforcement Learning provides 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,...
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 p. — 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...
John Wiley & Sons, Inc., 2025. — 416 p. — ISBN: 978-1-394-27255-6. 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, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. “Deep Reinforcement...
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...
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...
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...
Independent publication, 2024. — 224 p. 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....
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