Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach

Abstract

A growing demand is witnessed in both industry and academia for employing Deep Learning (DL) in various domains to solve real-world problems. Deep Reinforcement Learning (DRL) is the application of DL in the domain of Reinforcement Learning (RL). Like any software systems, DRL applications can fail because of faults in their programs. In this paper, we present the first attempt to categorize faults occurring in DRL programs. We manually analyzed 761 artifacts of DRL programs (from Stack Overflow posts and GitHub issues) developed using well-known DRL frameworks (OpenAI Gym, Dopamine, Keras-rl, Tensorforce) and identified faults reported by developers/users. We labeled and taxonomized the identified faults through several rounds of discussions. The resulting taxonomy is validated using an online survey with 19 developers/researchers. To allow for the automatic detection of faults in DRL programs, we have defined a meta-model of DRL programs and developed DRLinter, a model-based fault detection approach that leverages static analysis and graph transformations. The execution flow of DRLinter consists in parsing a DRL program to generate a model conforming to our meta-model and applying detection rules on the model to identify faults occurrences. The effectiveness of DRLinter is evaluated using 15 synthetic DRL programs in which we injected faults observed in the analyzed artifacts of the taxonomy. The results show that DRLinter can successfully detect faults in all synthetic faulty programs.

Bugs in Machine Learning-based Systems: A Faultload Benchmark

“…As an example, Denchmark [37] does not provide enough information to reproduce and trigger bugs. Meanwhile, several studies on the testing of ML-based systems have used synthetic bugs for assessment [54,52] which may bias their evaluation by hiding potential weaknesses. Some others have also used a limited number of real bugs [79,62] that may not be representative of a thorough evaluation, implying an incorrect measure of the proposed approach's reliability.…”