  Adversarial attacks have exposed a significant security vulnerability in
state-of-the-art machine learning models. Among these models include deep
reinforcement learning agents. The existing methods for attacking reinforcement
learning agents assume the adversary either has access to the target agent's
learned parameters or the environment that the agent interacts with. In this
work, we propose a new class of threat models, called snooping threat models,
that are unique to reinforcement learning. In these snooping threat models, the
adversary does not have the ability to interact with the target agent's
environment, and can only eavesdrop on the action and reward signals being
exchanged between agent and environment. We show that adversaries operating in
these highly constrained threat models can still launch devastating attacks
against the target agent by training proxy models on related tasks and
leveraging the transferability of adversarial examples.
