Learning for General Competency in Video Games
Michael Bowling, Marc G. Bellemare, Erik Talvitie, Joel Venes, Marlos C. Machado, Organizers
Technical Report WS-15-10
Softcover version of the technical report: $25.00 softcover
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Over recent years there has been a surge of interest in video game platforms as a source of challenging AI domains. The Atari 2600, for example, offers hundreds of independently-designed games drawn from a variety of genres. Through this variety, video game platforms offer the opportunity to truly test the general competency of learning agents. Unresolved challenges in these domains include learning dynamical models for high-dimensional visual observations, learning concise state representations, and efficient exploration when rewards are sparse.
The aim of this workshop is to accelerate the dissemination of interesting approaches, engineering techniques and lessons learned concerning the Atari 2600 and other video game domains. A portion of the workshop will also be devoted to a panel discussing evaluation standards to assist in reproducibility and comparability between different research groups.