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. This white paper argues for using RTS games as a benchmark for AI research, and describes the design and components of TorchCraft.

References in corpus (1)

• Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks

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• AI2-THOR: An Interactive 3D Environment for Visual AI
• Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
• A Survey of Deep Reinforcement Learning in Video Games
• Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play
• ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
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• The NetHack Learning Environment
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• Hierarchical Decision Making by Generating and Following Natural Language Instructions
• STARDATA: A StarCraft AI Research Dataset
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• Why Build an Assistant in Minecraft?
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• Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
• ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents
• Neural Fictitious Self-Play on ELF Mini-RTS
• Multi-platform Version of StarCraft: Brood War in a Docker Container: Technical Report
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