Training artificial intelligence models for radiation therapy

Abstract

Disclosed herein are systems and methods for iteratively training artificial intelligence models using reinforcement learning techniques. With each iteration, a training agent applies a random radiation therapy treatment attribute corresponding to the radiation therapy treatment attribute associated with previously performed radiation therapy treatments when an epsilon value indicative of a likelihood of exploration and exploitation training of the artificial intelligence model satisfies a threshold. When the epsilon value does not satisfy the threshold, the agent generates, using an existing policy, a first predicted radiation therapy treatment attribute, and generates, using a predefined model, a second predicted radiation therapy treatment attribute. The agent applies one of the first predicted radiation therapy treatment attribute or the second predicted radiation therapy treatment attribute that is associated with a higher reward. The agent iteratively repeats training the artificial intelligence model until the existing policy satisfies an accuracy threshold.