Near-Equivalent Q-learning Policies for Dynamic Treatment Regimes

ArXi:2603.19440v1 Announce Type: cross Precision medicine aims to tailor therapeutic decisions to individual patient characteristics. This objective is commonly formalized through dynamic treatment regimes, which use statistical and machine learning methods to derive sequential decision rules adapted to evolving clinical information. In most existing formulations, these approaches produce a single optimal treatment at each stage, leading to a unique decision sequence.