  This paper proposes a novel scheme for the watermarking of Deep Reinforcement
Learning (DRL) policies. This scheme provides a mechanism for the integration
of a unique identifier within the policy in the form of its response to a
designated sequence of state transitions, while incurring minimal impact on the
nominal performance of the policy. The applications of this watermarking scheme
include detection of unauthorized replications of proprietary policies, as well
as enabling the graceful interruption or termination of DRL activities by
authorized entities. We demonstrate the feasibility of our proposal via
experimental evaluation of watermarking a DQN policy trained in the Cartpole
environment.
