Introducing real world physics and macro-actions to general video game ai
Diego Pérez-Liébana, Matthew Caleb Stephenson, Raluca D. Gaina, Jochen Renz, Simon Mark Lucas · 2017
The General Video Game AI Framework has featured multiple games and several tracks since the first competition in 2014. Although the games of the framework are very assorted in nature, there is an underlying commonality with respect to the physics that govern the game: all of them are based on a grid where the sprites make discrete movements, which is not expressive enough to cover any meaningful physics. This paper introduces an enhanced physics system that brings real-world physics such as friction, inertia and other forces to the framework. We also introduce macro-actions for the first time in GVGAI in two different controllers, Rolling Horizon Evolution and Monte Carlo Tree Search. Their usefulness is demonstrated in a new set of games that exploits these new physics features. Our results show that macro-actions can help controllers in certain situations and games, although there is a strong dependency on the game played when selecting which configuration fits best.