  While significant research advances have been made in the field of deep
reinforcement learning, there have been no concrete adversarial attack
strategies in literature tailored for studying the vulnerability of deep
reinforcement learning algorithms to membership inference attacks. In such
attacking systems, the adversary targets the set of collected input data on
which the deep reinforcement learning algorithm has been trained. To address
this gap, we propose an adversarial attack framework designed for testing the
vulnerability of a state-of-the-art deep reinforcement learning algorithm to a
membership inference attack. In particular, we design a series of experiments
to investigate the impact of temporal correlation, which naturally exists in
reinforcement learning training data, on the probability of information
leakage. Moreover, we compare the performance of \emph{collective} and
\emph{individual} membership attacks against the deep reinforcement learning
algorithm. Experimental results show that the proposed adversarial attack
framework is surprisingly effective at inferring data with an accuracy
exceeding $84\%$ in individual and $97\%$ in collective modes in three
different continuous control Mujoco tasks, which raises serious privacy
concerns in this regard. Finally, we show that the learning state of the
reinforcement learning algorithm influences the level of privacy breaches
significantly.
