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CALVIN — a neural network that can learn to plan and navigate unknown environments
Shu Ishida
Summary: CALVIN is a neural network that can plan, explore and navigate in novel 3D environments. It learns tasks such as solving mazes, just by learning from expert demonstrations. Our work builds upon Value Iteration Networks (VIN) [1], a type of recurrent convolutional neural network that builds plans dynamically. While VINs work well in fully-known…
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Machine Learning DPhil Student at Visual Geometry Group (VGG) Oxford, researching in Planning, Spatial Reasoning and Reinforcement Learning
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