Reinforcement learning applied to games

Abstract

With the continuous evolution of computational power, especially in the computer graphics area, reinforcement learning has been gaining traction in the community as many novel methods are being created and older ones revamped. Many of these employ artificial neural networks, yet treat them as a black box system. In this paper, we start by introducing classic and recent developments in the area of machine learning, followed by an overview of the most relevant work done with deep neural networks applied to games. Our objective is to give the reader insights on how neural networks learn and clarify the decisions made in the development of modern systems.