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Testing Deep Learning (DL) systems is a complex task as they do not behavelike traditional systems would, notably because of their stochastic nature.Nonetheless, being able to adapt existing testing techniques such as MutationTesting (MT) to DL settings would greatly improve their potentialverifiability. While some efforts have been made to extend MT to the SupervisedLearning paradigm, little work has gone into extending it to ReinforcementLearning (RL) which is also an important component of the DL ecosystem butbehaves very differently from SL. This paper builds on the existing approach ofMT in order to propose a framework, RLMutation, for MT applied to RL. Notably,we use existing taxonomies of faults to build a set of mutation operatorsrelevant to RL and use a simple heuristic to generate test cases for RL. Thisallows us to compare different mutation killing definitions based on existingapproaches, as well as to analyze the behavior of the obtained mutationoperators and their potential combinations called Higher Order Mutation(s)(HOM). We show that the design choice of the mutation killing definition canaffect whether or not a mutation is killed as well as the generated test cases.Moreover, we found that even with a relatively small number of test cases andoperators we manage to generate HOM with interesting properties which canenhance testing capability in RL systems.

Mutation Testing of Deep Reinforcement Learning Based on Real Faults