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OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment by Lim, Yooseok; Lee, Sujee is a scholarly article available to read on EtoBox.

What is OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment about?

Accurate medication dosing holds an important position in the overall patient therapeutic process. Therefore, much research has been conducted to develop optimal administration strategy based on Reinforcement learning (RL). However, Relying solely on a few explicitly defined reward functions makes it difficult to learn a treatment strategy that encompasses the diverse characteristics of various patients. Moreover, the multitude of drugs utilized in clinical practice makes it infeasible to construct a dedicated reward function for each medication. Here, we tried to develop a reward network that captures clinicians' therapeutic intentions, departing from explicit rewards, and to derive an optimal heparin dosing policy. In this study, we introduce Offline Model-based Guided Reward Learning (OMG-RL), which performs offline inverse RL (IRL). Through OMG-RL, we learn a parameterized reward function that captures the expert's intentions from limited data, thereby enhancing the agent's policy. We validate the proposed approach on the heparin dosing task. We show that OMG-RL policy is positively reinforced not only in terms of the learned reward network but also in activated partial thrombo

Author

Lim, Yooseok; Lee, Sujee

Published

2024

Language

EN