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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). 

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.

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...

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...

Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This monograph surveys an integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps: dynamics model learning and planning-learning integration. In this comprehensive survey of the topic, the authors first cover dynamics model learning, including challenges such as dealing with stochasticity, uncertainty,...