  Imitation learning can reproduce policies by observing experts, which poses a
problem regarding policy privacy. Policies, such as human, or policies on
deployed robots, can all be cloned without consent from the owners. How can we
protect against external observers cloning our proprietary policies? To answer
this question we introduce a new reinforcement learning framework, where we
train an ensemble of near-optimal policies, whose demonstrations are guaranteed
to be useless for an external observer. We formulate this idea by a constrained
optimization problem, where the objective is to improve proprietary policies,
and at the same time deteriorate the virtual policy of an eventual external
observer. We design a tractable algorithm to solve this new optimization
problem by modifying the standard policy gradient algorithm. Our formulation
can be interpreted in lenses of confidentiality and adversarial behaviour,
which enables a broader perspective of this work. We demonstrate the existence
of "non-clonable" ensembles, providing a solution to the above optimization
problem, which is calculated by our modified policy gradient algorithm. To our
knowledge, this is the first work regarding the protection of policies in
Reinforcement Learning.
