Control-Tutored Reinforcement Learning

2019-12-12 · Francesco De Lellis, Fabrizia Auletta, Giovanni Russo, Piero De Lellis, Mario di Bernardo

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.

📄 PDF Abstract BibTeX arXiv:1912.06085

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reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Control-Tutored Reinforcement Learning: an application to the Herding Problem

2019-11-26 · Francesco De Lellis, Fabrizia Auletta, Giovanni Russo, Mario di Bernardo

In this extended abstract we introduce a novel control-tutored Q-learning approach (CTQL) as part of the ongoing effort in developing model-based and safe RL for continuous state spaces. We validate our approach by apply…

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We present an architecture where a feedback controller derived on an approximate model of the environment assists the learning process to enhance its data efficiency. This architecture, which we term as Control-Tutored Q…

OpenAI Gym Q-Learning Reinforcement Learning (RL)

Tutoring Reinforcement Learning via Feedback Control

2020-12-12 · Francesco De Lellis, Giovanni Russo, Mario di Bernardo

We introduce a control-tutored reinforcement learning (CTRL) algorithm. The idea is to enhance tabular learning algorithms by means of a control strategy with limited knowledge of the system model. By tutoring the learni…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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