Reinforcement Learning in Control Theory: A New Approach to Mathematical Problem Solving

Kala Agbo Bidi, Jean-Michel Coron, Amaury Hayat, Nathan Lichtlé

Keywords: Control theory; Reinforcement learning; AI for maths; stabilization; feedback control

TL;DR: We present a novel RL-based approach to find mathematical feedback controls for dynamics systems, on the example of the SIT system.

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.

Submission Number: 35