TY - BOOK AU - Plaat, Aske TI - Deep reinforcement learning SN - 9789811906374 U1 - 006.31 PY - 2022/// CY - Singapore PB - Springer KW - Machine learning KW - Artificial intelligence N1 - Table of contents: Front Matter Pages i-xv Download chapter PDF Introduction Aske Plaat Pages 1-24 Tabular Value-Based Reinforcement Learning Aske Plaat Pages 25-67 Deep Value-Based Reinforcement Learning Aske Plaat Pages 69-100 Policy-Based Reinforcement Learning Aske Plaat Pages 101-133 Model-Based Reinforcement Learning Aske Plaat Pages 135-167 Two-Agent Self-Play Aske Plaat Pages 169-218 Multi-Agent Reinforcement Learning Aske Plaat Pages 219-262 Hierarchical Reinforcement Learning Aske Plaat Pages 263-285 Meta-Learning Aske Plaat Pages 287-322 Further Developments Aske Plaat Pages 323-336 [https://link.springer.com/book/10.1007/978-981-19-0638-1] N2 - Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning. (https://link.springer.com/book/10.1007/978-981-19-0638-1) ER -