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Nitty-Gritty of Deep Reinforcement Learning for the Healthcare Sector

Nitty-Gritty of Deep Reinforcement Learning for the Healthcare Sector

Vaishnavi Kumari, Vandana Dubey, Priti Kumari, Rishabh Pal, Sarika Shrivastava, P. T. N. Anh
Copyright: © 2023 |Pages: 17
ISBN13: 9798369308769|ISBN13 Softcover: 9798369348390|EISBN13: 9798369308776
DOI: 10.4018/979-8-3693-0876-9.ch016
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MLA

Kumari, Vaishnavi, et al. "Nitty-Gritty of Deep Reinforcement Learning for the Healthcare Sector." AI and IoT-Based Technologies for Precision Medicine, edited by Alex Khang, IGI Global, 2023, pp. 263-279. https://doi.org/10.4018/979-8-3693-0876-9.ch016

APA

Kumari, V., Dubey, V., Kumari, P., Pal, R., Shrivastava, S., & Anh, P. T. (2023). Nitty-Gritty of Deep Reinforcement Learning for the Healthcare Sector. In A. Khang (Ed.), AI and IoT-Based Technologies for Precision Medicine (pp. 263-279). IGI Global. https://doi.org/10.4018/979-8-3693-0876-9.ch016

Chicago

Kumari, Vaishnavi, et al. "Nitty-Gritty of Deep Reinforcement Learning for the Healthcare Sector." In AI and IoT-Based Technologies for Precision Medicine, edited by Alex Khang, 263-279. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/979-8-3693-0876-9.ch016

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Abstract

Deep reinforcement learning (DRL) is one of the emerging areas of machine learning which focuses on maximized rewards. DRL is a type of machine learning that combines reinforcement learning and deep learning. It uses a series of algorithms to enable an agent to learn how to make decisions in a complex environment. DRL is a subset of artificial intelligence that focuses on making decisions based on the environment and the rewards associated with each action.The goal of DRL is to maximize the long-term reward of an agent. In order to do this, the agent must use a combination of deep learning, reinforcement learning and other AI techniques to learn which actions will lead to the highest reward. DRL is used to solve a variety of problems, from playing video games to controlling robots. It is also used in autonomous driving and robotics, as well as for financial trading. DRL is a powerful tool for solving complex problems and has been used in a variety of research projects. DRL has the potential to revolutionize the way we interact with machines and the environment.

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