using reinforcement learning methods to price a perishable product, case study: orange

‎determining the optimal selling price for different commodities has always been one of the main topics of scientific and industrial research‎. ‎perishable products have a short life and due to their deterioration over time‎, ‎they cause great damage if not managed‎. ‎many industries‎, ‎retailers‎, ‎and service providers have the opportunity to increase their revenue through optimal pricing of perishable products that must be sold within a certain period‎. ‎in the pricing issue‎, ‎a seller must determine the price of several units of a perishable or seasonal product to be sold for a limited time‎. ‎this article examines pricing policies that increase revenue for the sale of a given inventory with an expiration date‎. ‎booster learning algorithms are used to analyze how companies can simultaneously learn and optimize pricing strategy in response to buyers‎. ‎it is also shown that using reinforcement learning we can model a demand-dependent problem‎. ‎this paper presents an optimization method in a model-independent environment in which demand is learned and pricing decisions are updated at the moment‎. ‎we compare the performance of learning algorithms using monte carlo simulations‎.