# Portfolio Search and Optimization for General Strategy Game-Playing

arXiv: https://arxiv.org/abs/2104.10429 · HTML: https://mlsanity.com/paper/2104.10429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms](https://mlsanity.com/paper/1802.10363/) (2018)
- [The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation](https://mlsanity.com/paper/1802.05991/) (2018)
- [Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing](https://mlsanity.com/paper/1704.07075/) (2017)
- [The Design Of "Stratega": A General Strategy Games Framework](https://mlsanity.com/paper/2009.05643/) (2020)

## Built on by (most cited first, 1 of 1)

- [Elastic Monte Carlo Tree Search with State Abstraction for Strategy Game Playing](https://mlsanity.com/paper/2205.15126/) (2022)

## Abstract (arXiv)

Portfolio methods represent a simple but efficient type of action abstraction which has shown to improve the performance of search-based agents in a range of strategy games. We first review existing portfolio techniques and propose a new algorithm for optimization and action-selection based on the Rolling Horizon Evolutionary Algorithm. Moreover, a series of variants are developed to solve problems in different aspects. We further analyze the performance of discussed agents in a general strategy game-playing task. For this purpose, we run experiments on three different game-modes of the Stratega framework. For the optimization of the agents' parameters and portfolio sets we study the use of the N-tuple Bandit Evolutionary Algorithm. The resulting portfolio sets suggest a high diversity in play-styles while being able to consistently beat the sample agents. An analysis of the agents' performance shows that the proposed algorithm generalizes well to all game-modes and is able to outperform other portfolio methods.