A CBR/RL system for learning micromanagement in real-time strategy games

Abstract

The gameplay of real-time strategy games can be divided into macromanagement and micromanagement. Several researchers have studied automated learning for macromanagement, using a case-based reasoning/reinforcement learning architecture to defeat both static and dynamic opponents. Unlike the previous research, we present the Unit Priority Artificial Intelligence (UPAI). UPAI is a case-based reasoning/reinforcement learning system for learning the micromanagement task of prioritizing which enemy units to attack in different game situations, through unsupervised learning from experience. We discuss different case representations, as well as the exploration vs exploitation aspect of reinforcement learning in UPAI. Our research demonstrates that UPAI can learn to improve its micromanagement decisions, by defeating both static and dynamic opponents in a micromanagement setting.