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A review of reinforcement learning in chemistry

A review of reinforcement learning in chemistry
A review of reinforcement learning in chemistry
The growth of machine learning as a tool for research in computational chemistry is well documented. For many years, this growth was heavily driven by the paradigms of supervised and unsupervised learning. Recently, however, there has been increased interest in the use of a third paradigm: reinforcement learning. This approach, in which an agent interacts with an environment to learn which actions it should take to maximise a long-term objective, is particularly suited to problems of planning or sequential decision making. In this review, we present an accessible summary of the theory behind reinforcement learning (and its common extension, deep reinforcement learning) tailored specifically to chemistry researchers. We also review the applications of reinforcement learning which already exist within the world of chemistry, and consider the future direction of research based on this promising technique.
551-567
Gow, Stephen
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Niranjan, Mahesan
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Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Gow, Stephen
922171a1-6d31-4969-9e2e-8443daff9c0c
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f

Gow, Stephen, Niranjan, Mahesan, Kanza, Samantha and Frey, Jeremy G. (2022) A review of reinforcement learning in chemistry. Digital Discovery, 1 (5), 551-567. (doi:10.1039/D2DD00047D).

Record type: Article

Abstract

The growth of machine learning as a tool for research in computational chemistry is well documented. For many years, this growth was heavily driven by the paradigms of supervised and unsupervised learning. Recently, however, there has been increased interest in the use of a third paradigm: reinforcement learning. This approach, in which an agent interacts with an environment to learn which actions it should take to maximise a long-term objective, is particularly suited to problems of planning or sequential decision making. In this review, we present an accessible summary of the theory behind reinforcement learning (and its common extension, deep reinforcement learning) tailored specifically to chemistry researchers. We also review the applications of reinforcement learning which already exist within the world of chemistry, and consider the future direction of research based on this promising technique.

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Accepted/In Press date: 27 August 2022
e-pub ahead of print date: 30 August 2022
Published date: 1 October 2022
Additional Information: This work was supported by the AI for Scientific Discovery Network, funded by UKRI EPSRC under grant no: EP/S000356/1. The authors would like to thank Colin Bird for his work in proof-reading this manuscript and providing valuable feedback on its domain-specific content.

Identifiers

Local EPrints ID: 473578
URI: http://eprints.soton.ac.uk/id/eprint/473578
PURE UUID: fc7bd73c-8c7e-45b0-be8a-36c26a72d26d
ORCID for Stephen Gow: ORCID iD orcid.org/0000-0003-0121-1697
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

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Date deposited: 24 Jan 2023 17:32
Last modified: 17 Mar 2024 04:01

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Contributors

Author: Stephen Gow ORCID iD
Author: Mahesan Niranjan ORCID iD
Author: Samantha Kanza ORCID iD
Author: Jeremy G. Frey ORCID iD

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