Evolving micro for 3D Real-Time Strategy games

This paper extends prior work in generating two dimensional micro for Real-Time Strategy games to three dimensions. We extend our influence map and potential fields representation to three dimensions and compare two hill-climbers with a genetic algorithm on the problem of generating high performance influence map, potential field, and reactive control parameters that control the behavior of units in an open source Real-Time Strategy game. Results indicate that genetic algorithms evolve better behaviors for ranged units that inflict damage on enemies while kiting to avoid damage. Additionally, genetic algorithms evolve better behaviors for melee units that concentrate firepower on selective enemies to decrease the opposing army's effectiveness. Evolved behaviors, particularly for ranged units, generalize well to new scenarios. Our work thus provides evidence for the viability of an influence map and potential fields based representation for reactive control algorithms in games, 3D simulations, and aerial vehicle swarms.