Portfolio Search and Optimization for General Strategy Game-Playing
This work addresses performance enhancement for AI agents in general strategy games, representing an incremental improvement over existing portfolio methods.
The authors tackled the problem of improving search-based agents in strategy games by proposing a new portfolio optimization and action-selection algorithm based on the Rolling Horizon Evolutionary Algorithm, which consistently beat sample agents and outperformed other portfolio methods across three game-modes in the Stratega framework.
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.