Summary of Show, Don’t Tell: Learning Reward Machines From Demonstrations For Reinforcement Learning-based Cardiac Pacemaker Synthesis, by John Komp et al.
Show, Don’t Tell: Learning Reward Machines from Demonstrations for Reinforcement Learning-Based Cardiac Pacemaker Synthesis
by John Komp, Dananjay Srinivas, Maria Pacheco, Ashutosh Trivedi
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers explore the application of reinforcement learning (RL) in designing cardiac pacemakers that can adapt to individual patients’ needs. The authors propose using labeled demonstrations of patient-pacemaker interactions to learn correctness specifications for RL-based pacemaker design. To achieve this, they leverage advances in machine learning to extract signals from labeled electrocardiogram (ECG) recordings as reward machines using recurrent neural networks and transformer architectures. These reward machines are then used to train an RL agent that synthesizes a cardiac pacemaker based on the resulting specifications. The authors validate their approach by designing a simple pacemaker with RL and comparing its performance to existing pacemakers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses artificial intelligence (AI) to help design better pacemakers for people’s hearts. It’s like teaching a computer how to make a custom pacemaker just for someone who needs it. The researchers use special algorithms called reinforcement learning, which is like trial and error, but faster and more accurate. They take recordings of heartbeats from people with pacemakers and teach the computer what makes each person’s heartbeat different. Then, they let the computer design a new pacemaker that can work well for that specific person. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning » Transformer
