Show, Don't Tell: Learning Reward Machines from Demonstrations for Reinforcement Learning-Based Cardiac Pacemaker Synthesis

TL;DR

This paper introduces a method to learn reward machines from demonstrations for reinforcement learning in cardiac pacemaker design, simplifying specification capture and improving the synthesis process.

Contribution

It proposes a novel approach to automatically learn reward machines from expert demonstrations, reducing manual effort in pacemaker specification and enabling RL-based synthesis.

Findings

Successfully learned reward machines from electrocardiogram demonstrations.

Synthesized pacemakers that adhere to specifications derived from expert data.

Validated pacemaker designs using properties from official documentation.

Abstract

An (artificial cardiac) pacemaker is an implantable electronic device that sends electrical impulses to the heart to regulate the heartbeat. As the number of pacemaker users continues to rise, so does the demand for features with additional sensors, adaptability, and improved battery performance. Reinforcement learning (RL) has recently been proposed as a performant algorithm for creative design space exploration, adaptation, and statistical verification of cardiac pacemakers. The design of correct reward functions, expressed as a reward machine, is a key programming activity in this process. In 2007, Boston Scientific published a detailed description of their pacemaker specifications. This document has since formed the basis for several formal characterizations of pacemaker specifications using real-time automata and logic. However, because these translations are done manually, they are challenging to verify. Moreover, capturing requirements in automata or logic is notoriously difficult. We posit that it is significantly easier for domain experts, such as electrophysiologists, to observe and identify abnormalities in electrocardiograms that correspond to patient-pacemaker interactions. Therefore, we explore the possibility of learning correctness specifications from such labeled demonstrations in the form of a reward machine and training an RL agent to synthesize a cardiac pacemaker based on the resulting reward machine. We leverage advances in machine learning to extract signals from labeled demonstrations as reward machines using recurrent neural networks and transformer architectures. These reward machines are then used to design a simple pacemaker with RL. Finally, we validate the resulting pacemaker using properties extracted from the Boston Scientific document.