Multi-Agent Reinforcement Learning Model Simulation for Attention-Deficit Hyperactivity Disorder Children

Abstract

Background: A child with Attention-Deficit Hyperactivity Disorder (ADHD) faces two issues: inattention and hyperactivity/impulsivity. These two symptoms make the child’s life more challenging compared to non-ADHD individuals. Therefore, one of the steps toward better quality of life involves cooperation with and contact with the environment to better address this condition. Thanks to Artificial Intelligence (AI), doctors, caregivers, and parents are increasingly better able to understand the hardships these children face. One AI technique is Reinforcement Learning (RL). Methods: We propose an RL model simulation with 44 child agents with or without ADHD, using the Independent Deep Q Network (IDQN), Value Decomposition Network (VDN), and QMIX algorithms. Results: Comparing the results obtained with these three algorithms, children with ADHD find it more challenging to choose the maximum rewards than neurotypical children (395 at episode 300 for non-ADHD compared to 340 at episode 120 for ADHD using IDQN, 69 from episode 90 for ADHD compared to 82 for non-ADHD via VDN, and 31 at episode 110 for ADHD versus 28 at episode 110 for non-ADHD with QMIX). Conclusions: The simulated ADHD agents struggle to aim for the maximum rewards as much as neurotypical children.