Conference Papers Year : 2023

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.
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hal-04250601 , version 1 (19-10-2023)

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  • HAL Id : hal-04250601 , version 1

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Kala Agbo Bidi, Jean-Michel Coron, Amaury Hayat, Nathan Lichtlé. Reinforcement Learning in Control Theory: A New Approach to Mathematical Problem Solving. 3rd Workshop on Mathematical Reasoning and AI at NeurIPS'23, Dec 2023, New Orleans (LA), United States. ⟨hal-04250601⟩
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