A list of papers and links referencing the game of Starcraft. This list is meant as a reference for researchers to get caught up on different past techniques used in this game.

With the recently released API from joint work of Blizzard and Deepmind, Starcraft II seems poised to become an exciting testbed for Deep Learning and Artificial Intelligence research. Over the past 8 years, the original game, Starcraft: Brood War, has been used as a research platform through the BWAPI. In order for unexperienced researchers to properly transition to working on problems in Starcraft, they need to learn about past techniques in this field. That is the goal of this repository.

As a note, there is extensive research into reinforcement learning for game agents. Rather than attempting to compile a complete list of papers in this field, lets solely focus on papers working in or mentioning starcraft.

• Starcraft Bots
• Bot Competitions
• Brood War API
• Surveys
• Builder Orders
• Macro Agents
• Micro Agents
• Human Analysis of Bots
• Datasets
• Other

StarCraft II: A New Challenge for Reinforcement Learning (2017) [pdf]

• UAlbertaBot [web]
• StarCraft Bots and Competitions (2015), Churchill, et al. [pdf]

• AIIDE Starcraft AI Competition [web]
• IEEE Conference on Computational Intelligence and Games (CIG) [web]
• Student StarCraft AI (SSCAI) Tournament [web]
• BWAPI Bots Ladder [web]

• Brood War API [web]
• Torchcraft [web]
• Atlantis Java Wrapper [web]

• A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft (2013), Santiago Onta˜non, et al. [pdf]

• Continual Online Evolutionary Planning for In-Game Build Order Adaptation in StarCraft (2017), Justesen, et al. [pdf]
• SCFusion Build Order Optimizer [web]
• Builder Order Optimization in Starcraft (2011), Churchill, et al. [pdf]
• Using genetic algorithms to find Starcraft 2 build orders [[web]](Using genetic algorithms to find Starcraft 2 build orders)

• Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning (2017), Foerster, et al. [pdf]
• Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games (2017), Peng, et al. [pdf]
• Learning Macromanagement in StarCraft from Replays using Deep Learning (2016), Justesen, et al. [pdf]
• Building Human-Level AI for Real-Time Strategy Games (2011), Weber, et al. [pdf]
• Evolutionary Learning of Goal Priorities in a Real-Time Strategy Game (2012), Young, et al. [pdf]
• Applying Goal-Driven Autonomy to StarCraft (2010), Weber, et al. [pdf]

• Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks (2016), Usunier, et al. [pdf]
• Using Monte-Carlo Planning for Micro-Management in Starcraft (2011), Zhe, et al. [pdf]

• Evaluation of StarCraft Artificial Intelligence Competition Bots by Experienced Human Players (2016), Kim, et al. [pdf]

• STARDATA: A StarCraft AI Research Dataset (2017), Lin, et al. [pdf]

• RTS AI: Problems and Techniques (2015), Ontañón, et al. [pdf]
• A Bayesian model for opening prediction in RTS games with application to StarCraft (2011), Synnaeve, et al. [pdf]

We need your contributions!

This list is very incomplete, please feel free to edit and pull a request!