HackerPapers

arXiv:1708.02139cs.AI

STARDATA: A StarCraft AI Research Dataset

Zeming Lin, Jonas Gehring, Vasil Khalidov, Gabriel Synnaeve

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

We release a dataset of 65646 StarCraft replays that contains 1535 million frames and 496 million player actions. We provide full game state data along with the original replays that can be viewed in StarCraft. The game state data was recorded every 3 frames which ensures suitability for a wide variety of machine learning tasks such as strategy classification, inverse reinforcement learning, imitation learning, forward modeling, partial information extraction, and others. We use TorchCraft to extract and store the data, which standardizes the data format for both reading from replays and reading directly from the game. Furthermore, the data can be used on different operating systems and platforms. The dataset contains valid, non-corrupted replays only and its quality and diversity was ensured by a number of heuristics. We illustrate the diversity of the data with various statistics and provide examples of tasks that benefit from the dataset. We make the dataset available at https://github.com/TorchCraft/StarData . En Taro Adun!

arXiv comments
To be presented at AIIDE17
Journal reference
DOI
Subjects
cs.AIArtificial Intelligence

On Hacker News

Linked by 1 identifiable commenter in 1 comment across 1 thread, from Aug 2017 to Aug 2017. Found in 2 HN submissions from Aug 2017 to Aug 2017. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments1 thread

    • Related: Today I learned that a group of AI researchers has released a paper called: STARDATA: A StarCraft AI Research Dataset. According to one of the authors: "We're releasing a dataset of 65k StarCraft: Brood War games, 1.5b frames, 500m actions, 400GB of data. Check it out!"…

      dpflanView on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.