TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games

Abstract : We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from a machine learning framework, here Torch [9]. This white paper argues for using RTS games as a benchmark for AI research, and describes the design and components of TorchCraft.
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https://hal.archives-ouvertes.fr/hal-01436134
Contributor : Florian Richoux <>
Submitted on : Thursday, December 20, 2018 - 1:26:21 AM
Last modification on : Tuesday, March 26, 2019 - 9:25:22 AM
Document(s) archivé(s) le : Friday, March 22, 2019 - 10:57:40 AM

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  • HAL Id : hal-01436134, version 1
  • ARXIV : 1611.00625

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Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala, Timothée Lacroix, et al.. TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games. 2018. ⟨hal-01436134⟩

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