  Deep reinforcement learning (DRL) policies are vulnerable to unauthorized
replication attacks, where an adversary exploits imitation learning to
reproduce target policies from observed behavior. In this paper, we propose
Constrained Randomization of Policy (CRoP) as a mitigation technique against
such attacks. CRoP induces the execution of sub-optimal actions at random under
performance loss constraints. We present a parametric analysis of CRoP, address
the optimality of CRoP, and establish theoretical bounds on the adversarial
budget and the expectation of loss. Furthermore, we report the experimental
evaluation of CRoP in Atari environments under adversarial imitation, which
demonstrate the efficacy and feasibility of our proposed method against policy
replication attacks.
