Control-Tutored Reinforcement Learning

Francesco De Lellis; Fabrizia Auletta; Giovanni Russo; Piero De Lellis; Mario di Bernardo

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arXiv · 1912.06085

Control-Tutored Reinforcement Learning

Francesco De Lellis·Fabrizia Auletta·Giovanni Russo·Piero De Lellis·Mario di Bernardo

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Abstract

We introduce a control-tutored reinforcement learning (CTRL) algorithm. The idea is to enhance tabular learning algorithms so as to improve the exploration of the state-space, and substantially reduce learning times by leveraging some limited knowledge of the plant encoded into a tutoring model-based control strategy. We illustrate the benefits of our novel approach and its effectiveness by using the problem of controlling one or more agents to herd and contain within a goal region a set of target free-roving agents in the plane.

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BibTeX RIS

Francesco De Lellis, Fabrizia Auletta, Giovanni Russo, Piero De Lellis, Mario di Bernardo. 2019-12-12. Control-Tutored Reinforcement Learning. https://arxiv.org/abs/1912.06085

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