Machine learning has been widely applied to various applications, some of which involve training with privacy-sensitive data. A modest number of data breaches have been studied, including credit card information in natural language data and identities from face dataset. However, most of these studies focus on supervised learning models. As deep reinforcement learning (DRL) has been deployed in a number of real-world systems, such as indoor robot navigation, whether trained DRL policies can leak private information requires in-depth study. To explore such privacy breaches in general, we mainly propose two methods: environment dynamics search via genetic algorithm and candidate inference based on shadow policies. We conduct extensive experiments to demonstrate such privacy vulnerabilities in DRL under various settings. We leverage the proposed algorithms to infer floor plans from some trained Grid World navigation DRL agents with LiDAR perception. The proposed algorithm can correctly infer most of the floor plans and reaches an average recovery rate of 95.83% using policy gradient trained agents. In addition, we are able to recover the robot configuration in continuous control environments and an autonomous driving simulator with high accuracy. To the best of our knowledge, this is the first work to investigate privacy leakage in DRL settings and we show that DRL-based agents do potentially leak privacy-sensitive information from the trained policies.
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