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About this paper
Daria de Tinguy, Tim Verbelen, Bart Dhoedt
The model lets a navigating agent add predicted neighboring places to its internal map before visiting them. It uses position and boundary information to predict possible moves, then revises its map from observations. In simulated aliased 3×3 room exploration, it averaged 18.1±2 navigation steps to learn connectivity, versus 201.5±20 for the strongest tested CSCG variant; this result is limited to high-level room-grid inputs.
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