Neuroevolution for RTS Micro

TL;DR

This paper demonstrates that neuroevolution of augmenting topologies can effectively generate neural networks that control micro in RTS games, producing tactics like kiting and generalizing across scenarios.

Contribution

It introduces a neuroevolution approach to develop control policies for RTS micro, showcasing effective tactics and generalization capabilities.

Findings

Neural networks evolved via neuroevolution produce effective micro tactics.

Evolved networks exhibit kiting behavior similar to professional players.

The approach generalizes well to different starting positions and unit counts.

Abstract

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.