The profitability of manufacturers in multi-variant production is challenged by the combination of increasing customer requirements and volatile supply chains. A potential solution is dynamic pricing, where customers can select a delivery time and price based on their preferences, and demand can be balanced during peak times. This paper presents a dynamic pricing approach using an actor-critic reinforcement learning agent in combination with a production simulation model and applies it in the automation technology industry.