Evolving Effective Microbehaviors in Real-Time Strategy Games

Abstract

We investigate heuristic search algorithms to generate high-quality micromanagement in combat scenarios for real-time strategy (RTS) games. Macro- and micromanagement are two key aspects of RTS games. While good macro helps a player collect more resources and build more units, good micro helps a player win skirmishes and battles against equal numbers and types of opponent units or win even when outnumbered. In this paper, we use influence maps and potential fields as a basis representation to evolve short-term positioning and movement tactics. Unit microbehaviors in combat are compactly encoded into 14 parameters. A genetic algorithm evolves good microbehaviors by manipulating these 14 parameters. We compared the performance of our evolved ECSLBot with two other state-of-the-art bots, UAlbertaBot and Nova, on several skirmish scenarios in a popular RTS game StarCraft. The results show that the ECSLBot tuned by genetic algorithms outperforms UAlbertaBot and Nova in kiting efficiency, target selection, and fleeing. Further experiments show that the parameter values evolved in one scenario work well in other scenarios and that we can switch between preevolved parameter sets to perform well in unseen scenarios containing more than one type of opponent unit. We believe our representation and approach applied to each unit type of interest can result in effective microperformance against melee and ranged opponents and provides a viable approach toward complete RTS bots.