Portfolio Search and Optimization for General Strategy Game-Playing
  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.
