Comparing Two Representations for Evolving Micro in 3D RTS Games

We are interested in using genetic algorithms to generate winning maneuvering behaviors (or micro) in skirmish scenarios for three dimensional Real-Time Strategy games. In prior work, we encoded parameterized 3D micro behaviors like target selection and kiting into an algorithm for controlling friendly units in battle. Genetic algorithms then tuned these parameters to guide unit maneuvering in order to win skirmishes. In this study, we investigate a new representation for micro behaviors that uses only an influence map and a combination of thirteen potential fields. Genetic algorithms then tune influence map and potential field parameters to evolve winning micro behaviors. We compare the performance of both representations on identical scenarios against identical opponents in a full 3D RTS game environment called FastEcslent. The results show that the genetic algorithm using our new representation using less domain knowledge, reliably evolved high quality 3D micro behaviors that slightly, but significantly, outperformed behaviors from our prior work. Our work thus provides evidence for the viability of using potential fields for generating high quality, complex, micro for three dimensional RTS games.