TY - JOUR
T1 - Deep learning for video game playing
AU - Justesen, Niels
AU - Bontrager, Philip
AU - Togelius, Julian
AU - Risi, Sebastian
PY - 2019
Y1 - 2019
N2 - In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards.
AB - In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards.
KW - learning
KW - Algorithms
KW - machine learning algorithms
KW - multilayer neural network
KW - artificial intelligence
KW - learning
KW - Algorithms
KW - machine learning algorithms
KW - multilayer neural network
KW - artificial intelligence
U2 - 10.1109/TG.2019.2896986
DO - 10.1109/TG.2019.2896986
M3 - Journal article
JO - IEEE Transactions on Games
JF - IEEE Transactions on Games
ER -