From predicting to decision making: Reinforcement learning in biomedicine

Reinforcement learning (RL) is one important branch of artificial intelligence (AI), which intuitively imitates the learning style of human beings. It is commonly derived from solving game playing problems and is extensively used for decision‐making, control and optimization problems. It has been ex...

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Vydané v:Wiley interdisciplinary reviews. Computational molecular science Ročník 14; číslo 4; s. e1723 - n/a
Hlavní autori: Liu, Xuhan, Zhang, Jun, Hou, Zhonghuai, Yang, Yi Isaac, Gao, Yi Qin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken, USA Wiley Periodicals, Inc 01.07.2024
Wiley Subscription Services, Inc
Predmet:
Algorithms
Artificial intelligence
Bioaccumulation
Biological activity
Biological effects
Biological properties
Data science
Decision making
drug design
Learning algorithms
Machine learning
macromolecule
molecular simulation
Predictive control
reaction planning
Reinforcement
reinforcement learning
ISSN:1759-0876, 1759-0884
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Shrnutí:Reinforcement learning (RL) is one important branch of artificial intelligence (AI), which intuitively imitates the learning style of human beings. It is commonly derived from solving game playing problems and is extensively used for decision‐making, control and optimization problems. It has been extensively applied for solving complicated problems with the property of Markov decision‐making processes. With data accumulation and comprehensive analysis, researchers are not only satisfied with predicting the results for experimental systems but also hope to design or control them for the sake of obtaining the desired properties or functions. RL is potentially facilitated to solve a large number of complicated biological and chemical problems, because they could be decomposed into multi‐step decision‐making process. In practice, substantial progress has been made in the application of RL to the field of biomedicine. In this paper, we will first give a brief description about RL, including its definition, basic theory and different type of methods. Then we will review some detailed applications in various domains, for example, molecular design, reaction planning, molecular simulation and etc. In the end, we will summarize the essentialities of RL approaches to solve more diverse problems compared with other machine learning methods and also outlook the possible trends to overcome their limitations in the future. This article is categorized under: Data Science > Chemoinformatics Data Science > Computer Algorithms and Programming Data Science > Artificial Intelligence/Machine Learning The interplay between the agent and environment in reinforcement learning.
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ISSN:1759-0876
1759-0884
DOI:10.1002/wcms.1723