Real-World Reinforcement Learning: Observations from Two Successful Cases

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Date
2021-06-23
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Language
en
Pages
285
273
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Proceedings of the 34th Bled eConference: Digital Support from Crisis to Progressive Change
Abstract
Reinforcement Learning (RL) is a machine learning technique that enables artificial agents to learn optimal strategies for sequential decision-making problems. RL has achieved superhuman performance in artificial domains, yet real-world applications remain rare. We explore the drivers of successful RL adoption for solving practical business problems. We rely on publicly available secondary data on two cases: data center cooling at Google and trade order execution at JPMorgan. We perform thematic analysis using a pre-defined coding framework based on the known challenges to real-world RL by DulacArnold, Mankowitz, & Hester (2019). First, we find that RL works best when the problem dynamics can be simulated. Second, the ability to encode the desired agent behavior as a reward function is critical. Third, safety constraints are often necessary in the context of trial-and-error learning. Our work is amongst the first in Information Systems to discuss the practical business value of the emerging AI subfield of RL.
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Keywords
reinforcement learning, AI adoption, thematic analysis, machine learning, self-learning agents
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Citation
Back , P 2021 , Real-World Reinforcement Learning: Observations from Two Successful Cases . in Proceedings of the 34th Bled eConference: Digital Support from Crisis to Progressive Change . Univerza v Mariboru , pp. 273 , Bled eConference , Virtual, Online , 27/06/2021 . https://doi.org/10.18690/978-961-286-485-9.20