Investigating Human Priors for Playing Video Games

February 28, 2018 Β· Declared Dead Β· πŸ› International Conference on Machine Learning

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Authors Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L. Griffiths, Alexei A. Efros arXiv ID 1802.10217 Category cs.AI: Artificial Intelligence Cross-listed cs.LG Citations 151 Venue International Conference on Machine Learning Last Checked 2 months ago
Abstract
What makes humans so good at solving seemingly complex video games? Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games. Given a sample game, we conduct a series of ablation studies to quantify the importance of various priors on human performance. We do this by modifying the video game environment to systematically mask different types of visual information that could be used by humans as priors. We find that removal of some prior knowledge causes a drastic degradation in the speed with which human players solve the game, e.g. from 2 minutes to over 20 minutes. Furthermore, our results indicate that general priors, such as the importance of objects and visual consistency, are critical for efficient game-play. Videos and the game manipulations are available at https://rach0012.github.io/humanRL_website/
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