Learning to optimally rank and personalize search results is a difficult and important topic in scientific information retrieval as well as in online retail business, where we typically want to bias customer query results with respect to specific preferences for the purpose of increasing revenue. Reinforcement learning, as a generic-flexible learning model, is able to bias, e.g. personalize, learning-to-rank results at scale, so that externally specified goals, e.g. an increase in sales and probably revenue, can be achieved. This article introduces the topics learning-to-rank and reinforcement learning in a problem-specific way and is accompanied by the example project ‚cli-ranker‘, a command line tool utilizing reinforcement learning principles for learning user information retrieval preferences regarding text document ranking.