Game Engine Learning from Video
Full Text: 0518.pdf
Intelligent agents need to be able to make predictions about their environment. In this work we present a novel approach to learn a forward simulation model via simple search over pixel input. We make use of a video game, Super Mario Bros., as an initial test of our approach as it represents a physics system that is significantly less complex than reality. We demonstrate the significant improvement of our approach in predicting future states compared with a baseline CNN and apply the learned model to train a game playing agent. Thus we evaluate the algorithm in terms of the accuracy and value of its output model.
Citation
M. Guzdial, B. Li, M. Riedl. "Game Engine Learning from Video". International Joint Conference on Artificial Intelligence (IJCAI), (ed: Carles Sierra), pp 3707-3713, August 2017.| Keywords: | |
| Category: | In Conference |
| Web Links: | IJCAI |
BibTeX
@incollection{Guzdial+al:IJCAI17,
author = {Matthew Guzdial and Boyang Li and Mark Riedl},
title = {Game Engine Learning from Video},
Editor = {Carles Sierra},
Pages = {3707-3713},
booktitle = {International Joint Conference on Artificial Intelligence
(IJCAI)},
year = 2017,
}Last Updated: October 29, 2020Submitted by Sabina P
