Binary Space Partitioning as Intrinsic Reward

Wojciech Skaba

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arXiv · 1804.03611

Binary Space Partitioning as Intrinsic Reward

Wojciech Skaba

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Abstract

An autonomous agent embodied in a humanoid robot, in order to learn from the overwhelming flow of raw and noisy sensory, has to effectively reduce the high spatial-temporal data dimensionality. In this paper we propose a novel method of unsupervised feature extraction and selection with binary space partitioning, followed by a computation of information gain that is interpreted as intrinsic reward, then applied as immediate-reward signal for the reinforcement-learning. The space partitioning is executed by tiny codelets running on a simulated Turing Machine. The features are represented by concept nodes arranged in a hierarchy, in which those of a lower level become the input vectors of a higher level.

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Wojciech Skaba. 2018-04-10. Binary Space Partitioning as Intrinsic Reward. https://arxiv.org/abs/1804.03611

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