Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control
Abstract
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-learning (CTQL), is presented in two alternative flavours. The former is based on defining the reward function so that a Boolean condition can be used to determine when the control tutor policy is adopted, while the latter, termed as probabilistic CTQL (pCTQL), is instead based on executing calls to the tutor with a certain probability during learning. Both approaches are validated, and thoroughly benchmarked against Q-Learning, by considering the stabilization of an inverted pendulum as defined in OpenAI Gym as a representative problem.
Cite
Text
De Lellis et al. "Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control." Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022.Markdown
[De Lellis et al. "Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control." Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022.](https://mlanthology.org/l4dc/2022/lellis2022l4dc-controltutored/)BibTeX
@inproceedings{lellis2022l4dc-controltutored,
title = {{Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control}},
author = {De Lellis, Francesco and Coraggio, Marco and Russo, Giovanni and Musolesi, Mirco and Bernardo, Mario},
booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference},
year = {2022},
pages = {1048-1059},
volume = {168},
url = {https://mlanthology.org/l4dc/2022/lellis2022l4dc-controltutored/}
}