A reinforcement learning application of guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapy

no code implementations • 14 Apr 2020 • Azar Sadeghnejad-Barkousaraie, Gyanendra Bohara, Steve Jiang, Dan Nguyen

We propose a reinforcement learning strategy using Monte Carlo Tree Search capable of finding a superior beam orientation set and in less time than CG. We utilized a reinforcement learning structure involving a supervised learning network to guide Monte Carlo tree search (GTS) to explore the decision space of beam orientation selection problem.

Anatomy reinforcement-learning +1