Reinforcement Learning in Control Theory: A New Approach to Mathematical Problem Solving
Abstract
One of the central questions in control theory is achieving stability through feedback control. This paper introduces a novel approach that combines Reinforcement Learning (RL) with mathematical analysis to address this challenge, with a specific focus on the Sterile Insect Technique (SIT) system. The objective is to find a feedback control that stabilizes the mosquito population model. Despite the mathematical complexities and the absence of known solutions for this specific problem, our RL approach identifies a candidate solution for an explicit stabilizing control. This study underscores the synergy between AI and mathematics, opening new avenues for tackling intricate mathematical problems.
Cite
Text
Bidi et al. "Reinforcement Learning in Control Theory: A New Approach to Mathematical Problem Solving." NeurIPS 2023 Workshops: MATH-AI, 2023.Markdown
[Bidi et al. "Reinforcement Learning in Control Theory: A New Approach to Mathematical Problem Solving." NeurIPS 2023 Workshops: MATH-AI, 2023.](https://mlanthology.org/neuripsw/2023/bidi2023neuripsw-reinforcement/)BibTeX
@inproceedings{bidi2023neuripsw-reinforcement,
title = {{Reinforcement Learning in Control Theory: A New Approach to Mathematical Problem Solving}},
author = {Bidi, Kala Agbo and Coron, Jean-Michel and Hayat, Amaury and Lichtlé, Nathan},
booktitle = {NeurIPS 2023 Workshops: MATH-AI},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/bidi2023neuripsw-reinforcement/}
}