Portfolio Search and Optimization for General Strategy Game-Playing

• URL: http://arxiv.org/abs/2104.10429v1
• Date: Wed, 21 Apr 2021 09:28:28 GMT
• Title: Portfolio Search and Optimization for General Strategy Game-Playing
• Authors: Alexander Dockhorn, Jorge Hurtado-Grueso, Dominik Jeurissen, Linjie Xu, Diego Perez-Liebana
• Abstract summary: We propose a new algorithm for optimization and action-selection based on the Rolling Horizon Evolutionary Algorithm. For the optimization of the agents' parameters and portfolio sets we study the use of the N-tuple Bandit Evolutionary Algorithm. 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.
• Score: 58.896302717975445
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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