Portfolio Search and Optimization for General Strategy Game-Playing

TL;DR

This paper introduces a new portfolio optimization algorithm using Rolling Horizon Evolutionary Algorithm for strategy game-playing, demonstrating improved performance and diversity across multiple game modes.

Contribution

It proposes a novel portfolio optimization method based on the Rolling Horizon Evolutionary Algorithm and analyzes its effectiveness in general strategy game-playing.

Findings

The new algorithm outperforms existing portfolio methods.

Portfolio sets exhibit high diversity in play-styles.

The approach generalizes well across different game modes.

Abstract

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.