Guest Editorial Special Issue on Deep/Reinforcement Learning and Games

Abstract

Deep learning (DL) and reinforcement learning (RL) have been applied with great success to many games, including Go and Atari 2600 games. Monte Carlo Tree Search (MCTS), developed in 2006, can be viewed as a kind of online RL. This technique has greatly improved the level of Go-playing programs. MCTS has since become the state of the art for many other games including Hex, Havannah, and general game playing, and has found much success in applications as diverse as scheduling, unit commitment problems, and probabilistic planning. DL has transformed fields such as image and video recognition and speech understanding. In computer games, DL started making its mark in 2014, when teams from the University of Edinburgh and Google DeepMind independently applied deep convolutional neural networks (DCNNs) to the problem of expertmove prediction in Go.Clark and Storkey’s DCNN achieved a move prediction rate of 44%, exceeding all previously published results. DeepMind’s publication followed soon after, with a DCNN that reached 55%. The combination of DL and RL led to great advances in Atari 2600 game playing, and to the ultimate breakthrough in computer Go. In 2017, DeepMind proposed a new deep reinforcement learning (DRL) algorithm and developed AlphaGo Zero, which is significant for not requiring any human knowledge of Go. By removing the requirement for domain knowledge, DRL is also flexible in that the method can be applied to a wide range of games and problems, ushering in a variety of new research opportunities. In this special issue, we are delighted to bring you eight articles on applying DL/RL related techniques to games research.