Article citationsMore>>
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S. and Hassabis, D. (2015) Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529-533.
https://doi.org/10.1038/nature14236
has been cited by the following article:
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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.
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