# Population Seeding Techniques for Rolling Horizon Evolution in General Video Game Playing

## TL;DR

This paper explores population seeding techniques to enhance Rolling Horizon Evolution in general video game playing, demonstrating significant performance improvements and competitive results against Monte Carlo Tree Search across multiple games.

## Contribution

It introduces population seeding methods, One Step Look Ahead and Monte Carlo Tree Search, for Rolling Horizon Evolution in general video games, showing their effectiveness over baseline methods.

## Key findings

- Seeding improves performance significantly over vanilla evolution.
- Seeding methods can match Monte Carlo Tree Search performance.
- Results are consistent across multiple games and parameters.

## Abstract

While Monte Carlo Tree Search and closely related methods have dominated General Video Game Playing, recent research has demonstrated the promise of Rolling Horizon Evolutionary Algorithms as an interesting alternative. However, there is little attention paid to population initialization techniques in the setting of general real-time video games. Therefore, this paper proposes the use of population seeding to improve the performance of Rolling Horizon Evolution and presents the results of two methods, One Step Look Ahead and Monte Carlo Tree Search, tested on 20 games of the General Video Game AI corpus with multiple evolution parameter values (population size and individual length). An in-depth analysis is carried out between the results of the seeding methods and the vanilla Rolling Horizon Evolution. In addition, the paper presents a comparison to a Monte Carlo Tree Search algorithm. The results are promising, with seeding able to boost performance significantly over baseline evolution and even match the high level of play obtained by the Monte Carlo Tree Search.