TY  - JOUR
A1  - Kastius, Alexander
A1  - Schlosser, Rainer
T1  - Dynamic pricing under competition using reinforcement learning
JF  - Journal of revenue and pricing management
N2  - 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.
KW  - Dynamic pricing
KW  - Competition
KW  - Reinforcement learning
KW  - E-commerce
KW  - Price collusion
Y1  - 2021
U6  - https://doi.org/10.1057/s41272-021-00285-3
SN  - 1476-6930
SN  - 1477-657X
VL  - 21
IS  - 1
SP  - 50
EP  - 63
PB  - Springer Nature Switzerland AG
CY  - Cham
ER  - 
TY  - JOUR
A1  - Schlosser, Rainer
A1  - Boissier, Martin
T1  - Dealing with the dimensionality curse in dynamic pricing competition
BT  - Using frequent repricing to compensate imperfect market anticipations
JF  - Computers & Operations Research
N2  - Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustments can be time-consuming. We analyze stochastic dynamic pricing models under oligopoly competition for the sale of perishable goods. To circumvent the curse of dimensionality, we propose a heuristic approach to efficiently compute price adjustments. To demonstrate our strategy’s applicability even if the number of competitors is large and their strategies are unknown, we consider different competitive settings in which competitors frequently and strategically adjust their prices. For all settings, we verify that our heuristic strategy yields promising results. We compare the performance of our heuristic against upper bounds, which are obtained by optimal strategies that take advantage of perfect price anticipations. We find that price adjustment frequencies can have a larger impact on expected profits than price anticipations. Finally, our approach has been applied on Amazon for the sale of used books. We have used a seller’s historical market data to calibrate our model. Sales results show that our data-driven strategy outperforms the rule-based strategy of an experienced seller by a profit increase of more than 20%.
KW  - Dynamic pricing
KW  - Oligopoly competition
KW  - Dynamic programming
KW  - Data-driven strategies
KW  - E-commerce
Y1  - 2018
U6  - https://doi.org/10.1016/j.cor.2018.07.011
SN  - 0305-0548
SN  - 1873-765X
VL  - 100
SP  - 26
EP  - 42
PB  - Elsevier
CY  - Oxford
ER  -