TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala,, Timoth\'ee Lacroix, Zeming Lin, Florian Richoux, Nicolas Usunier

TL;DR
TorchCraft is a library that facilitates deep learning research on RTS games like StarCraft by integrating game control with the Torch framework, promoting RTS games as valuable AI benchmarks.
Contribution
It introduces TorchCraft, a novel library that simplifies interfacing RTS games with deep learning frameworks, enabling new AI research avenues.
Findings
Eases integration of RTS games with deep learning frameworks
Supports research on RTS games as AI benchmarks
Provides tools for controlling and analyzing RTS gameplay
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. This white paper argues for using RTS games as a benchmark for AI research, and describes the design and components of TorchCraft.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsArtificial Intelligence in Games · Simulation Techniques and Applications · Reinforcement Learning in Robotics
