Options Discovery with Budgeted Reinforcement Learning

Aurélia Léon; Ludovic Denoyer

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arXiv · 1611.06824

Options Discovery with Budgeted Reinforcement Learning

Aurélia Léon·Ludovic Denoyer

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Abstract

We consider the problem of learning hierarchical policies for Reinforcement Learning able to discover options, an option corresponding to a sub-policy over a set of primitive actions. Different models have been proposed during the last decade that usually rely on a predefined set of options. We specifically address the problem of automatically discovering options in decision processes. We describe a new learning model called Budgeted Option Neural Network (BONN) able to discover options based on a budgeted learning objective. The BONN model is evaluated on different classical RL problems, demonstrating both quantitative and qualitative interesting results.

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Aurélia Léon, Ludovic Denoyer. 2016-11-21. Options Discovery with Budgeted Reinforcement Learning. https://arxiv.org/abs/1611.06824

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