[KDD 2020] [Best Paper Runner-up | Research Track] Malicious Attacks against Deep Reinforcement Learning Interpretations
Aug 13, 2020113 views
The past years have witnessed the rapid development of deep reinforcement learning (DRL), which is a combination of deep learning,and reinforcement learning (RL). However, the adoption of deep,neural networks makes the decision-making process of DRL opaque,and lacking transparency. Motivated by this, various interpretation,methods for DRL have been proposed. However, those interpretation methods make an implicit assumption that they are performed,in a reliable and secure environment. In practice, sequential agentenvironment interactions expose the DRL algorithms and their,corresponding downstream interpretations to extra adversarial risk.,In spite of the prevalence of malicious attacks, there is no existing,work studying the possibility and feasibility of malicious attacks,against DRL interpretations. To bridge this gap, in this paper, we,investigate the vulnerability of DRL interpretation methods. Specifically, we introduce the first study of the adversarial attacks against,DRL interpretations, and propose an optimization framework based,on which the optimal adversarial attack strategy can be derived. In,addition, we study the vulnerability of DRL interpretation methods to the model poisoning attacks, and present an algorithmic,framework to rigorously formulate the proposed model poisoning,attack. Finally, we conduct both theoretical analysis and extensive,experiments to validate the effectiveness of the proposed malicious,attacks against DRL interpretations.