Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/287698 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Journal of Revenue and Pricing Management [ISSN:] 1477-657X [Volume:] 21 [Issue:] 1 [Publisher:] Palgrave Macmillan UK [Place:] London [Year:] 2021 [Pages:] 50-63
Verlag: 
Palgrave Macmillan UK, London
Zusammenfassung: 
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.
Schlagwörter: 
Dynamic pricing
Competition
Reinforcement learning
E-commerce
Price collusion
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