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Learning a world model and planning with a self-organizing, dynamic neural system

Marc Toussaint
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Abstract

We present a connectionist architecture that can learn a model of the relations between perceptions and actions and use this model for behavior planning. State representations are learned with a growing self-organizing layer which is directly coupled to a perception and a motor layer. Knowledge about possible state transitions is encoded in the lateral connectivity. Motor signals modulate this lateral connectivity and a dynamic field on the layer organizes a planning process. All mechanisms are local and adaptation is based on Hebbian ideas. The model is continuous in the action, perception, and time domain.

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Marc Toussaint. 2003-06-11. Learning a world model and planning with a self-organizing, dynamic neural system. https://arxiv.org/abs/nlin/0306015

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