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.
| Verfasserangaben: | Alexander Kastius, Rainer SchlosserORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1057/s41272-021-00285-3 |
| ISSN: | 1476-6930 |
| ISSN: | 1477-657X |
| Titel des übergeordneten Werks (Englisch): | Journal of revenue and pricing management |
| Verlag: | Springer Nature Switzerland AG |
| Verlagsort: | Cham |
| Publikationstyp: | Wissenschaftlicher Artikel |
| Sprache: | Englisch |
| Datum der Erstveröffentlichung: | 27.02.2021 |
| Erscheinungsjahr: | 2022 |
| Datum der Freischaltung: | 09.02.2023 |
| Freies Schlagwort / Tag: | Competition; Dynamic pricing; E-commerce; Price collusion; Reinforcement learning |
| Band: | 21 |
| Ausgabe: | 1 |
| Seitenanzahl: | 14 |
| Erste Seite: | 50 |
| Letzte Seite: | 63 |
| Fördernde Institution: | Projekt DEAL |
| Organisationseinheiten: | An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH |
| DDC-Klassifikation: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
| Peer Review: | Referiert |
| Publikationsweg: | Open Access / Hybrid Open-Access |
| Lizenz (Deutsch): |  CC-BY - Namensnennung 4.0 International |
