Artikel
Dynamic pricing under competition using reinforcement learning
Dynamic pricing is considered a possibility to gain an advantage over competitors in modern online markets. The past advancements in Reinforcement Learning (RL) provided more capable algorithms that can be used to solve pricing problems. In this paper, we study the performance of Deep Q-Networks (DQN) and Soft Actor Critic (SAC) in different market models. We consider tractable duopoly settings, where optimal solutions derived by dynamic programming techniques can be used for verification, as well as oligopoly settings, which are usually intractable due to the curse of dimensionality. We find that both algorithms provide reasonable results, while SAC performs better than DQN. Moreover, we show that under certain conditions, RL algorithms can be forced into collusion by their competitors without direct communication.
- Sprache
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Englisch
- Erschienen in
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Journal: Journal of Revenue and Pricing Management ; ISSN: 1477-657X ; Volume: 21 ; Year: 2021 ; Issue: 1 ; Pages: 50-63 ; London: Palgrave Macmillan UK
- Klassifikation
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Wirtschaft
- Thema
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Dynamic pricing
Competition
Reinforcement learning
E-commerce
Price collusion
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Kastius, Alexander
Schlosser, Rainer
- Ereignis
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Veröffentlichung
- (wer)
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Palgrave Macmillan UK
- (wo)
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London
- (wann)
-
2021
- DOI
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doi:10.1057/s41272-021-00285-3
- Letzte Aktualisierung
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10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Kastius, Alexander
- Schlosser, Rainer
- Palgrave Macmillan UK
Entstanden
- 2021
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