  Counterfactual instances are a powerful tool to obtain valuable insights into
automated decision processes, describing the necessary minimal changes in the
input space to alter the prediction towards a desired target. Most previous
approaches require a separate, computationally expensive optimization procedure
per instance, making them impractical for both large amounts of data and
high-dimensional data. Moreover, these methods are often restricted to certain
subclasses of machine learning models (e.g. differentiable or tree-based
models). In this work, we propose a deep reinforcement learning approach that
transforms the optimization procedure into an end-to-end learnable process,
allowing us to generate batches of counterfactual instances in a single forward
pass. Our experiments on real-world data show that our method i) is
model-agnostic (does not assume differentiability), relying only on feedback
from model predictions; ii) allows for generating target-conditional
counterfactual instances; iii) allows for flexible feature range constraints
for numerical and categorical attributes, including the immutability of
protected features (e.g. gender, race); iv) is easily extended to other data
modalities such as images.
