mlsanityData to 14 Oct 2026

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

arXiv 1611.00625 ↗First posted 1 Nov 2016cs.LG cs.AIcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Nov 2016.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 4; 2022: 3; 2023: 1; 2024: 2; 2025: 1.

12 papers.

  1. 2022SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learningcited by 122
  2. 2022A Survey of Multi-Agent Deep Reinforcement Learning with Communicationcited by 104
  3. 2018Learning a Prior over Intent via Meta-Inverse Reinforcement Learningcited by 36
  4. 2021Core Challenges in Embodied Vision-Language Planningcited by 30
  5. 2022Learning to Generalize with Object-centric Agents in the Open World Survival Game Craftercited by 13
  6. 2024ING-VP: MLLMs cannot Play Easy Vision-based Games Yetcited by 11
  7. 2025HLSMAC: A New StarCraft Multi-Agent Challenge for High-Level Strategic Decision-Makingcited by 2
  8. 2021Rethinking of AlphaStarcited by 1
  9. 2024AMaze: An intuitive benchmark generator for fast prototyping of generalizable agentscited by 1
  10. 2021Simultaneous Navigation and Construction Benchmarking Environmentscited by 0
  11. 2021Truthful Self-Playcited by 0
  12. 2023Core Challenges in Embodied Vision-Language Planningcited by 0