Dynamic Price Allocation and Optimization for E-Commerce Platforms Using Reinforcement Learning and Deep Learning

The concept of dynamic pricing has already become one of the most significant aspects of electronic commerce, which is constantly changing in terms of the level of demand and competition, as well as the response of customers to a specific product (or service). Conventional methods of pricing and reinforcement learning methods like Deep Q-Networks (DQN) tend to have restricted flexibility, dis- crete action, and no proper estimation of demand. Our uncertainty-aware dynamic pricing framework as offered in this paper incorporates a hybrid demands forecasting, Transformer-LSTM demand forecasting model and Soft-Actor-Critic (SAC) reinforcement learning to optimize prices continuously. The Trans- former component models the long-range time interdependencies whereas the LSTM models the sequential nature of demand patterns and allows it to predict the demand robustly and precisely. The state representation of the SAC agent, which learns the optimal pricing policies under dynamic market, takes these forecasts into consideration.

The suggested system is implemented on a scalable, API-focused system of microservices and allows making real-time pricing decisions. Online Retail II Evaluation The experimental analysis of the Online Retail II data reveals that the improvement of experimental approaches is substantial as compared to the baseline techniques. Demand forecasting with the model has an R 2 of 0.62 with a Mean Absolute Percentage Error (MAPE) of 8.7% and one can increase the revenue by 21.4% and the profit by 18.2% over the expected traditional methods of reinforcement learning.

The findings demonstrate the efficacy of melding cutting-edge deep learning and reinforcement learning approaches to scal- able, adaptable, and smart pricing in the practical e-commerce setting.

Dynamic Pricing, Reinforcement Learning, Transformer, Long short term memory (LSTM), Soft actor critic (SAC)

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