(good) decision early in the process is beneficial, for example if the patient needs urgent treatment or when it is resourceful to acquire additional information (e.g., lab tests, medical diagnostics). Hence, a first challenge would be to navigate through the trade-off of gathering sufficient information while doing so as quickly as possible. Considering this example, it is also straightforward to realize that it would be highly sub-optimal to always select the same small subset of input features, regardless of the patient and the complaint. Indeed, when a 83 year old male complains about headache, we would expect the doctor to choose a different investigation path, comparing to a case of 7 year old girl complaining on stomachache. Put differently, it is often desirable to select the input features adaptively. Moreover, in cases like this it is also natural to select features iteratively, so that the $k$’th feature is selected after the values of the previous $k-1$ selected ones are known.

In this manuscript we aim at these desired attributes and propose a novel reinforcement learning (RL)-based approach for adaptive and iterative feature selection. In our RL framework, the state corresponds to the current agent’s perception of the input sample, and 