Dynamic pricing under competition using reinforcement learning

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Revenue and Pricing Management ; ISSN: 1477-657X ; Volume: 21 ; Year: 2021 ; Issue: 1 ; Pages: 50-63 ; London: Palgrave Macmillan UK

Classification and Topics

• Classification: Wirtschaft
• Subject: Dynamic pricing ; Competition ; Reinforcement learning ; E-commerce ; Price collusion

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Kastius, Alexander ; Schlosser, Rainer
• Event: Veröffentlichung
• (who): Palgrave Macmillan UK
• (where): London
• (when): 2021

Further information

• DOI: doi:10.1057/s41272-021-00285-3
• Last update: 10.03.2025, 11:43 AM CET