Article

Towards Superhuman Imitation Learning for Sequential Head-and-Neck Cancer Treatment Decisions

2025-12-15

Abstract excerpt

We propose a simulator-driven imitation learning framework for sequential decision making in head and neck cancer (HNC) treatment. Our method, Superhuman Policy Gradient Optimization (SPGO) , integrates inverse reinforcement learning principles with policy gradient updates to derive three-stage treatment policies directly from recorded physician decisions. It leverages a pre-trained clinical simulator—combining a...

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Literature Corpus work: 57c010ba-ec4d-55b9-a05a-bfffef2412dd
DOI: 10.64898/2025.12.11.25342119

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