Deep Reinforcement Learning

Deep reinforcement learning has achieved impressive results in various fields, such as autonomous driving, game playing, molecular recombination, and robotics. It is inspired by biology and psychology and has the potential to revolutionize artificial intelligence. This book provides an overview of the field for a graduate course in artificial intelligence, covering basic algorithms and advanced topics.

Format: Paperback / softback
Length: 406 pages
Publication date: 12 June 2022
Publisher: Springer Verlag, Singapore

Deep reinforcement learning has garnered significant attention in recent times, resulting in impressive achievements across diverse fields such as autonomous driving, game playing, molecular recombination, and robotics. Computer programs have demonstrated remarkable capabilities by teaching themselves to understand previously challenging problems. In the game of Go, for instance, the program AlphaGo has surpassed three of the world's top players, showcasing its exceptional skills.

Deep reinforcement learning draws inspiration from the domains of biology and psychology. Biology has influenced the development of artificial neural networks and deep learning, while psychology explores the learning processes of animals and humans, as well as the reinforcement of desired behaviors through positive and negative stimuli. When we observe how reinforcement learning trains a simulated robot to walk, we are reminded of the way children learn through playful exploration. Techniques inspired by biology and psychology have proven highly effective in computers, serving as new blueprints for science and engineering. In fact, computers exhibit certain aspects of human behavior, making this field a central component of the pursuit of artificial intelligence.

These groundbreaking research advancements have not gone unnoticed by educators. Numerous universities have started offering courses on deep reinforcement learning, catering to the needs of graduate students in artificial intelligence. The purpose of this book is to offer a comprehensive overview of the field, providing the appropriate level of detail for a graduate-level course in artificial intelligence. It covers a wide range of topics, from the fundamental algorithms of Deep Q-learning to advanced subjects like multi-agent reinforcement learning and meta learning.

In conclusion, deep reinforcement learning has emerged as a transformative technology with the potential to revolutionize various industries and solve complex problems. Its roots in biology and psychology have provided valuable insights into the learning processes of animals and humans, and its applications in computer programs have demonstrated remarkable capabilities. As educators continue to recognize the significance of this field, more universities are offering courses on deep reinforcement learning, preparing students for the future of artificial intelligence.

Weight: 646g
Dimension: 154 x 235 x 28 (mm)
ISBN-13: 9789811906374
Edition number: 1st ed. 2022