MEIR KALECH

Deep Reinforcement Learning (DRL) is often used to generate control policies for autonomous agents. These policies are trained to control agents when they operate normally. Thus, unexpected faults may cause agents controlled using these policies to fail. When this occurs, it is important to understand and explain the root cause of such failures. In this paper, we define this diagnosis problem under different settings and assumptions. We also provide a benchmark suite for evaluating algorithms for solving this problem based on environments from AI Gym, a popular DRL framework.