A Novel Dynamic Pricing Method Based on Reinforcement Learning

Dynamic pricing is a hot topic in the data application-driven industry and academia in the emerging data security market. Traditional pricing methods cannot capture the value changes in the time dimension and cannot be flexibly adjusted according to market changes. The Deep Q-Network (DQN) in reinforcement learning also has an overestimation bias in the target value calculation process. To address the above challenges, we propose a dynamic pricing model combined with a reinforcement learning framework. This model can cleverly apply the time perspective of data streams to the actual data market. Firstly, we design models including static and dynamic price profit maximization. Then, we propose a new dynamic pricing method by combining the Double Deep Q-Network (DDQN) with the Noisy Network (Noisy Net) to monitor market environment changes in real time and improve the overall efficiency of the data market. To verify the advantages and effectiveness of this method, we generate consumer sample data additionally for experimental observation and compare the capabilities of static and dynamic pricing methods. A large number of comparative experimental results demonstrate the effectiveness of our proposed method and its excellent performance in dynamic pricing scenarios.