A Comparative Tutorial of Bayesian Sequential Design and Reinforcement Learning.
Reinforcement learning (RL) is a computational approach to reward-driven learning in sequential decision problems. It implements the discovery of optimal actions by learning from an agent interacting with an environment rather than from supervised data. We contrast and compare RL with traditional se...
| Publicado en: | American Statistician Vol. 77; no. 2; pp. 223 - 234 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
May2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=163317562&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 163317562 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: May2023 vid: 77 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 163317562 10.1080/00031305.2022.2129787 ppf: 223 ppct: 11 formats: tig: atl: A Comparative Tutorial of Bayesian Sequential Design and Reinforcement Learning. aug: au: Tec, Mauricio Duan, Yunshan Müller, Peter affil: Department of Biostatistics, Harvard T.H. Chan School of Public Health, Cambridge, MA Department of Statistics and Data Science, The University of Texas at Austin, Austin, TX su: Reward (Psychology) Active learning Optimal stopping (Mathematical statistics) Reinforcement learning Sequential learning Experimental design Statistical decision making sug: subj: Reward (Psychology) Active learning Optimal stopping (Mathematical statistics) Reinforcement learning Sequential learning Experimental design Statistical decision making keyword: Bayesian methods Bayesian methods ab: Reinforcement learning (RL) is a computational approach to reward-driven learning in sequential decision problems. It implements the discovery of optimal actions by learning from an agent interacting with an environment rather than from supervised data. We contrast and compare RL with traditional sequential design, focusing on simulation-based Bayesian sequential design (BSD). Recently, there has been an increasing interest in RL techniques for healthcare applications. We introduce two related applications as motivating examples. In both applications, the sequential nature of the decisions is restricted to sequential stopping. Rather than a comprehensive survey, the focus of the discussion is on solutions using standard tools for these two relatively simple sequential stopping problems. Both problems are inspired by adaptive clinical trial design. We use examples to explain the terminology and mathematical background that underlie each framework and map one to the other. The implementations and results illustrate the many similarities between RL and BSD. The results motivate the discussion of the potential strengths and limitations of each approach. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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