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The Cartographer
HoME: a Household Multimodal Environment
November 29, 2017 Β· Declared Dead Β· π International Conference on Learning Representations
"No code URL or promise found in abstract"
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Authors
Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron Courville
arXiv ID
1711.11017
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.CL,
cs.CV,
cs.RO,
cs.SD,
eess.AS
Citations
105
Venue
International Conference on Learning Representations
Last Checked
3 months ago
Abstract
We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting.
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