Rolling Horizon Evolutionary Algorithms for General Video Game Playing
This work addresses the challenge of parameter tuning for game-playing algorithms, which is incremental as it builds on existing RHEA methods to improve performance in video game AI.
The paper tackled the problem of optimizing Rolling Horizon Evolutionary Algorithms (RHEA) for general video game playing by combining all known modifications and using a parameter optimizer to find the best configurations across 20 games, resulting in new state-of-the-art solutions on several games.
Game-playing Evolutionary Algorithms, specifically Rolling Horizon Evolutionary Algorithms, have recently managed to beat the state of the art in win rate across many video games. However, the best results in a game are highly dependent on the specific configuration of modifications and hybrids introduced over several papers, each adding additional parameters to the core algorithm. Further, the best previously published parameters have been found from only a few human-picked combinations, as the possibility space has grown beyond exhaustive search. This paper presents the state of the art in Rolling Horizon Evolutionary Algorithms, combining all modifications described in literature, as well as new ones, for a large resultant hybrid. We then use a parameter optimiser, the N-Tuple Bandit Evolutionary Algorithm, to find the best combination of parameters in 20 games from the General Video Game AI Framework. Further, we analyse the algorithm's parameters and some interesting combinations revealed through the optimisation process. Lastly, we find new state of the art solutions on several games by automatically exploring the large parameter space of RHEA.