Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing
This work addresses the need for understanding evolutionary algorithms in general video game playing, offering incremental insights for researchers in AI and game playing.
The study analyzed the vanilla Rolling Horizon Evolution algorithm's parameters (population size and individual length) across 20 games in the General Video Game AI corpus, finding that in some configurations it outperforms Monte Carlo Tree Search, with distinctions made between deterministic and stochastic games.
Monte Carlo Tree Search techniques have generally dominated General Video Game Playing, but recent research has started looking at Evolutionary Algorithms and their potential at matching Tree Search level of play or even outperforming these methods. Online or Rolling Horizon Evolution is one of the options available to evolve sequences of actions for planning in General Video Game Playing, but no research has been done up to date that explores the capabilities of the vanilla version of this algorithm in multiple games. This study aims to critically analyse the different configurations regarding population size and individual length in a set of 20 games from the General Video Game AI corpus. Distinctions are made between deterministic and stochastic games, and the implications of using superior time budgets are studied. Results show that there is scope for the use of these techniques, which in some configurations outperform Monte Carlo Tree Search, and also suggest that further research in these methods could boost their performance.