Title: Deep Reinforcement Learning based Insight Selection Policy. Abstract: We live in the era of ubiquitous sensing and computing. More and more data is being collected and processed from devices, sensors and systems. This opens up opportunities to discover patterns from these data that could help in gaining better understanding into the source that produces them. This is useful in a wide range of domains, especially in the area of personal health, in which such knowledge could help in allowing users to comprehend their behaviour and indirectly improve their lifestyle. Insight generators are systems that identify such patterns and verbalise them in a readable text format, referred to as insights. The selection of insights is done using a scoring algorithm which aims at optimizing this process based on multiple objectives, e.g., factual correctness, usefulness and interestingness of insights. In this paper, we propose a novel Reinforcement Learning (RL) framework for insight selection where the scoring model is trained by user feedback on interestingness and their lifestyle quality estimates. With the use of highly reusable and simple principles of automatic user simulation based on real data, we demonstrate in this preliminary study that the RL solution may improve the selection of insights towards multiple pre-defined objectives.