Principled Option Learning in Markov Decision Processes

Roy Fox; Michal Moshkovitz; Naftali Tishby

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arXiv · 1609.05524

Principled Option Learning in Markov Decision Processes

Roy Fox·Michal Moshkovitz·Naftali Tishby

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Abstract

It is well known that options can make planning more efficient, among their many benefits. Thus far, algorithms for autonomously discovering a set of useful options were heuristic. Naturally, a principled way of finding a set of useful options may be more promising and insightful. In this paper we suggest a mathematical characterization of good sets of options using tools from information theory. This characterization enables us to find conditions for a set of options to be optimal and an algorithm that outputs a useful set of options and illustrate the proposed algorithm in simulation.

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Roy Fox, Michal Moshkovitz, Naftali Tishby. 2016-09-18. Principled Option Learning in Markov Decision Processes. https://arxiv.org/abs/1609.05524

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