Dynamic Pricing Algorithm Based on Deep Reinforcement Learning

Deep reinforcement learning has received widespread attention in recent years and has achieved significant success in various fields Due to the fact that real-world environments typically involve multiple agents interacting with the environment, multi-agent deep reinforcement learning has flourished and achieved excellent performance in complex sequential decision-making tasks in various fields. In order to solve the problem of dynamic pricing under unknown demand functions, this paper constructs a deep reinforcement learning framework with the goal of maximizing expected returns to find the optimal dynamic pricing strategy. Firstly, taking the service industry as a case study, a model and method for the decision-making process of revenue management were constructed, and the Markov property of revenue management problems was defined; Furthermore, a program was developed to conduct experiments, comparing and analyzing the experimental results of revenue management methods based on deep reinforcement learning with traditional methods The results indicate that the dynamic pricing strategy of intelligent agents can flexibly adjust prices at different levels of demand, and its performance is significantly better than traditional strategies. This article provides a theoretical basis for enterprises to make precise pricing and marketing decisions in the context of big data.