Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

Mokhaled N A Al-Hamadani; Mohammed A Fadhel; Laith Alzubaidi; Harangi Balazs

doi:10.3390/s24082461

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

Sensors (Basel). 2024 Apr 11;24(8):2461. doi: 10.3390/s24082461.

Authors

Mokhaled N A Al-Hamadani^{1

2

3}, Mohammed A Fadhel⁴, Laith Alzubaidi^{4

5

6}, Harangi Balazs¹

Affiliations

¹ Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, H-4032 Debrecen, Hungary.
² Doctoral School of Informatics, University of Debrecen, H-4032 Debrecen, Hungary.
³ Department of Electronic Techniques, Technical Institute/Alhawija, Northern Technical University, 36001 Kirkuk, Iraq.
⁴ Research and Development Department, Akunah Company, Brisbane, QLD 4120, Australia.
⁵ School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia.
⁶ Centre for Data Science, Queensland University of Technology, Brisbane, QLD 4000, Australia.

Abstract

Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential decision-making problems with simultaneous sampling, evaluation, and feedback. As a result, RL techniques have become suitable candidates for developing powerful solutions in various domains. In this study, we present a comprehensive and systematic review of RL algorithms and applications. This review commences with an exploration of the foundations of RL and proceeds to examine each algorithm in detail, concluding with a comparative analysis of RL algorithms based on several criteria. This review then extends to two key applications of RL: robotics and healthcare. In robotics manipulation, RL enhances precision and adaptability in tasks such as object grasping and autonomous learning. In healthcare, this review turns its focus to the realm of cell growth problems, clarifying how RL has provided a data-driven approach for optimizing the growth of cell cultures and the development of therapeutic solutions. This review offers a comprehensive overview, shedding light on the evolving landscape of RL and its potential in two diverse yet interconnected fields.

Keywords: Monte Carlo; cell growth; dynamic programming; object grasping and manipulation; reinforcement learning; temporal difference.

Publication types

Systematic Review
Review

MeSH terms

Algorithms*
Artificial Intelligence*
Delivery of Health Care*
Humans
Machine Learning
Robotics* / methods

Grants and funding

This work was supported by the project TKP2021-NKTA-34, implemented with the support provided by the National Research, Development, and Innovation Fund of Hungary under the TKP2021-NKTA funding scheme. Laith Alzubaidi would like to acknowledge the support received through the following funding schemes of the Australian Government: Australian Research Council (ARC) Industrial Transformation Training Centre (ITTC) for Joint Biomechanics under Grant IC190100020.