In this textbook the author takes as inspiration recent breakthroughs in game playing to explain how and why deep reinforcement learning works. In particular he shows why two-person games of tactics and strategy fascinate scientists, programmers, and game enthusiasts and unite them in a common goal: to create artificial intelligence (AI). After an introduction to the core concepts, environment, and communities of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach throughout, with Python code examples and exercises that help the reader understand how AI learns to play. He also supports the main text with detailed pointers to online machine learning frameworks, technical details for AlphaGo, notes on how to play and program Go and chess, and a comprehensive bibliography. The content is class-tested and suitable for advanced undergraduate and graduate courses on artificial intelligence and games. It's also appropriate for self-study by professionals engaged with applications of machine learning and with games development. Finally it's valuable for any reader engaged with the philosophical implications of artificial and general intelligence, games represent a modern Turing test of the power and limitations of AI.

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.

This book constitutes the thoroughly refereed post-conference proceedings of the 8th International Conference on Computers and Games, CG 2013, held in Yokohama, Japan, in August 2013, in conjunction with the 17th Computer and Games Tournament and the 20th World Computer-Chess Championship. The 21 papers presented were carefully reviewed and selected for inclusion in this book. They cover a wide range of topics which are grouped into five classes: Monte Carlo Tree Search and its enhancements; solving and searching; analysis of game characteristic; new approaches; and serious games.

This book constitutes the thoroughly refereed post-conference proceedings of the 13th Advances in Computer Games Conference, ACG 2011, held in Tilburg, The Netherlands, in November 2011. The 29 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics such as Monte-Carlo tree search and its enhancement, temporal difference learning, optimization, solving and searching, analysis of a game characteristic, new approaches, and serious games.

This book constitutes the thoroughly refereed post-conference proceedings of the 9th International Conference on Computers and Games, CG 2016, held in Leiden, The Netherlands,in conjunction with the 19th Computer Olympiad and the 22nd World Computer-Chess Championship. The 20 papers presented were carefully reviewed and selected of 30 submitted papers. The 20 papers cover a wide range of computer games and many different research topics in four main classes which determined the order of publication: Monte Carlo Tree Search (MCTS) and its enhancements (seven papers), concrete games (seven papers), theoretical aspects and complexity (five papers) and cognition model (one paper). The paper Using Partial Tablebases in Breakthrough by Andrew Isaac and Richard Lorentz received the Best Paper Award.

This book constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Advances in Computer Games, ACG 2015, held in Leiden, The Netherlands, in July 2015. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers cover a wide range of topics such as Monte-Carlo Tree Search and its enhancements; theoretical aspects and complexity; analysis of game characteristics; search algorithms; and machine learning.