Learning Reward Machines from Demonstrations to Synthesize Reinforcement Learning-Based Cardiac Pacemakers

This paper presents a novel approach to designing cardiac pacemakers by leveraging expert demonstrations to train reinforcement learning agents, eliminating the need for manual translation of requirements into formal logic.