Saripalli, V.R. (2019) Scalable and Data Efficient Deep Reinforcement Learning Methods for Healthcare Applications. Master’s Thesis, Colorado State University, Fort Collins.
https://aspenuniversity.idm.oclc.org/login?url=https://www.proquest.com/dissertations-theses/scalable-data-efficient-deep-reinforcement/docview/2349665414/se-2?accountid=34574

has been cited by the following article:

• TITLE: Toward Artificial General Intelligence: Deep Reinforcement Learning Method to AI in Medicine

  AUTHORS: Daniel Schilling Weiss Nguyen, Richard Odigie

  KEYWORDS: Artificial Intelligence, Deep Learning, General-Purpose Learning Agent, Generalizability, Algorithmic Flexibility, Internal Autonomy

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.9, September 28, 2023

  ABSTRACT: Artificial general intelligence (AGI) is the ability of an artificial intelligence (AI) agent to solve somewhat-arbitrary tasks in somewhat-arbitrary environments. Despite being a long-standing goal in the field of AI, achieving AGI remains elusive. In this study, we empirically assessed the generalizability of AI agents by applying a deep reinforcement learning (DRL) approach to the medical domain. Our investigation involved examining how modifying the agent’s structure, task, and environment impacts its generality. Sample: An NIH chest X-ray dataset with 112,120 images and 15 medical conditions. We evaluated the agent’s performance on binary and multiclass classification tasks through a baseline model, a convolutional neural network model, a deep Q network model, and a proximal policy optimization model. Results: Our results suggest that DRL agents with the algorithmic flexibility to autonomously vary their macro/microstructures can generalize better across given tasks and environments.