AgentFuzz: Fuzzing for Deep Reinforcement Learning Systems

Abstract

In recent years, deep reinforcement learning (DRL) technology has developed rapidly, and the application of DRL has been extended to many fields such as game gaming, au-tonomous driving, financial transactions, and robot control. As DRL applications expand and enrich, quality assurance of DRL software is increasingly important, especially in safety -critical areas. Therefore, it is necessary and urgent to adequately test DRL models to ensure the reliability and security of DRL systems. However, due to fundamental differences, traditional software testing methods cannot be directly applied to D RL systems. To bridge this gap, we introduce a new DRL system testing framework in this proposal, which aims to generate various test cases that can cause D RL systems to fail. The proposed testing framework is the first fuzzing framework for systematically testing DRL systems which we call AgentFuzz.