A Review of Real-Time Strategy Game AI

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games (2016, methodology): “As the scope of this paper is not to give a review of RTS AI research, we refer the reader to these surveys about existing research on RTS and StarCraft AI [16, 17].”
StarCraft II: A New Challenge for Reinforcement Learning (2017, background): “[21] and Robertson & Watson [25] for an overview.”
StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning (2018, background): “In recent years, the study of StarCraft AI has an impressive progress, driven by some StarCraft AI competitions and Brood War Application Programming Interface (BWAPI)1 [10].”
Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings (2020, background): “Despite such complexity and mechanism, the methodological explorations on AI systems for MOBA-game-playing are still very limited [5].”
Mastering Complex Control in MOBA Games with Deep Reinforcement Learning (2019, background): “This work is also related to research on building AI agents for playing StarCraft 1v1 games which have been significantly explored (Ontanón et al. 2013; Robertson and Watson 2014).”