Curiosity-driven reinforcement learning with homeostatic regulation

Ildefons Magrans de Abril; Ryota Kanai

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arXiv · 1801.07440

Curiosity-driven reinforcement learning with homeostatic regulation

Ildefons Magrans de Abril·Ryota Kanai

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Abstract

We propose a curiosity reward based on information theory principles and consistent with the animal instinct to maintain certain critical parameters within a bounded range. Our experimental validation shows the added value of the additional homeostatic drive to enhance the overall information gain of a reinforcement learning agent interacting with a complex environment using continuous actions. Our method builds upon two ideas: i) To take advantage of a new Bellman-like equation of information gain and ii) to simplify the computation of the local rewards by avoiding the approximation of complex distributions over continuous states and actions.

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Ildefons Magrans de Abril, Ryota Kanai. 2018-01-23. Curiosity-driven reinforcement learning with homeostatic regulation. https://arxiv.org/abs/1801.07440

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