Neuroevolution for RTS Micro
This work addresses the challenge of automating tactical control in real-time strategy games for game developers and AI researchers, but it is incremental as it applies an existing neuroevolution method to a specific game scenario.
The paper tackled the problem of evolving neural networks for controlling unit tactics (micro) in real-time strategy games, specifically for ranged units against melee units, and found that neuroevolution of augmenting topologies effectively generated networks that exhibited kiting behavior and generalized to different scenarios.
This paper uses neuroevolution of augmenting topologies to evolve control tactics for groups of units in real-time strategy games. In such games, players build economies to generate armies composed of multiple types of units with different attack and movement characteristics to combat each other. This paper evolves neural networks to control movement and attack commands, also called micro, for a group of ranged units skirmishing with a group of melee units. Our results show that neuroevolution of augmenting topologies can effectively generate neural networks capable of good micro for our ranged units against a group of hand-coded melee units. The evolved neural networks lead to kiting behavior for the ranged units which is a common tactic used by professional players in ranged versus melee skirmishes in popular real-time strategy games like Starcraft. The evolved neural networks also generalized well to other starting positions and numbers of units. We believe these results indicate the potential of neuroevolution for generating effective micro in real-time strategy games.