Sales applications are characterized by competitive settings and changing market environments. Hence, prices have to be adjusted frequently. We analyze stochastic dynamic pricing models under competition for the sale of durable goods. Given a competitor's pricing strategy, we show how to derive...

Most sales applications are characterized by competitive settings and limited demand information. Due to the complexity of such markets, smart pricing strategies are hard to derive. We analyze stochastic dynamic pricing models under competition for the sale of durable goods. In a first step, a...

Most sales applications are characterized by competitive settings and limited demand information. Due to the complexity of such markets, smart pricing strategies are hard to derive. We analyze stochastic dynamic pricing models under oligopoly competition for the sale of perishable goods. We...

Dynamic pricing is considered a possibility to gain an advantage over competitors in modern online markets. The past advancements in Reinforcement Learning (RL) provided more capable algorithms that can be used to solve pricing problems. In this paper, we study the performance of Deep Q-Networks...