# Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing

arXiv: https://arxiv.org/abs/1704.07075 · HTML: https://mlsanity.com/paper/1704.07075/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2017.

## Built on by (most cited first, 3 of 3)

- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Portfolio Search and Optimization for General Strategy Game-Playing](https://mlsanity.com/paper/2104.10429/) (2021)
- [Visualising Multiplayer Game Spaces](https://mlsanity.com/paper/2202.05773/) (2022)

## Abstract (arXiv)

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.