Dynamic Pricing Strategy for Data Product Through Deep Reinforcement Learning

With the rapid development of the data trading market, traditional fixed pricing strategies can no longer effectively reflect the real value of data products, thereby restricting the development of the data trading market. To address this challenge, this paper proposes a dynamic pricing method for data products based on deep reinforcement learning, aiming to attract buyers through dynamic pricing strategies and maximize the cumulative profits of data sellers, thus driving the further development of the data trading market. First, the dynamic pricing problem for data products is modeled as a Markov Decision Process (MDP). Then, a dynamic pricing algorithm based on the Deep Q-learning algorithm is designed, incorporating an annealing mechanism to optimize the exploration strategy. Finally, the performance of traditional reinforcement learning algorithms, specifically Q-learning and SARSA, is compared. The experimental results show that the dynamic pricing method based on deep reinforcement learning not only effectively enhances the cumulative profits of sellers but also significantly improves both revenue and algorithm performance compared to static pricing strategies and traditional reinforcement learning algorithms. This research provides a novel solution for data product pricing, contributing to the healthy development of the digital economy.