# Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best

arXiv: https://arxiv.org/abs/1901.00723 · HTML: https://mlsanity.com/paper/1901.00723/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2019.

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

- [Generating Diverse and Competitive Play-Styles for Strategy Games](https://mlsanity.com/paper/2104.08641/) (2021)
- [Predictive Control Using Learned State Space Models via Rolling Horizon Evolution](https://mlsanity.com/paper/2106.13911/) (2021)
- [Visualising Multiplayer Game Spaces](https://mlsanity.com/paper/2202.05773/) (2022)

## Abstract (arXiv)

This paper introduces a simple and fast variant of Planet Wars as a test-bed for statistical planning based Game AI agents, and for noisy hyper-parameter optimisation. Planet Wars is a real-time strategy game with simple rules but complex game-play. The variant introduced in this paper is designed for speed to enable efficient experimentation, and also for a fixed action space to enable practical inter-operability with General Video Game AI agents. If we treat the game as a win-loss game (which is standard), then this leads to challenging noisy optimisation problems both in tuning agents to play the game, and in tuning game parameters. Here we focus on the problem of tuning an agent, and report results using the recently developed N-Tuple Bandit Evolutionary Algorithm and a number of other optimisers, including Sequential Model-based Algorithm Configuration (SMAC). Results indicate that the N-Tuple Bandit Evolutionary offers competitive performance as well as insight into the effects of combinations of parameter choices.