Asymmetric multiagent reinforcement learning in pricing applications

Two pricing problems are solved by using asymmetric multiagent reinforcement learning methods in this paper. In the first problem, a flat pricing scenario, there are two competing brokers that sell identical products to customers and compete on the basis of price. The second problem is a hierarchical pricing scenario, where a supplier sells products to two competing brokers. In both cases, the methods converged and led to very promising results. We present a brief literature survey of pricing models based on reinforcement learning, introduce the basic concepts of Markov games and solve two pricing problems based on multiagent reinforcement learning.