  This paper investigates a class of attacks targeting the confidentiality
aspect of security in Deep Reinforcement Learning (DRL) policies. Recent
research have established the vulnerability of supervised machine learning
models (e.g., classifiers) to model extraction attacks. Such attacks leverage
the loosely-restricted ability of the attacker to iteratively query the model
for labels, thereby allowing for the forging of a labeled dataset which can be
used to train a replica of the original model. In this work, we demonstrate the
feasibility of exploiting imitation learning techniques in launching model
extraction attacks on DRL agents. Furthermore, we develop proof-of-concept
attacks that leverage such techniques for black-box attacks against the
integrity of DRL policies. We also present a discussion on potential solution
concepts for mitigation techniques.
