Dynamic pricing under competition using reinforcement learning

Alexander Kastius · Rainer Schlößer

92 citations0/0 files storedJournal of Revenue and Pricing Management

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Supply Chain and Inventory Management Merger and Competition Analysis Consumer Market Behavior and Pricing

Abstract

Abstract 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.

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References

2020Artificial Intelligence, Algorithmic Pricing, and Collusion 2015Dynamic pricing and learning: Historical origins, current research, and new directions 2002Inventory management in supply chains: a reinforcement learning approach 2002A reinforcement learning approach to a single leg airline revenue management problem with multiple fare classes and overbooking

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