Dynamic pricing model of E-commerce platforms based on deep reinforcement learning

Abstract

With the continuous development of artificial intelligence technology, its application field has gradually expanded. To further apply the deep reinforcement learning technology to the field of dynamic pricing, we build an intelligent dynamic pricing system, introduce the reinforcement learning technology related to dynamic pricing, and introduce existing research on the number of suppliers (single supplier and multiple suppliers), environmental models, and selection algorithms. A two-period dynamic pricing game model is designed to assess the optimal pricing strategy for e-commerce platforms under two market conditions and two consumer participation conditions. The first step is to analyze the pricing strategies of e-commerce platforms in mature markets, analyze the optimal pricing and profits of various enterprises under different strategy combinations, compare different market equilibriums and solve the Nash equilibrium. Then, assuming that all consumers are naive in the market, the pricing strategy of the duopoly e-commerce platform in emerging markets is analyzed. By comparing and analyzing the optimal pricing and total profit of each enterprise under different strategy combinations, the subgame refined Nash equilibrium is solved. Finally, assuming that the market includes all experienced consumers, the pricing strategy of the duopoly e-commerce platform in emerging markets is analyzed.