  Safety-critical robot systems need thorough testing to expose design flaws
and software bugs which could endanger humans. Testing in simulation is
becoming increasingly popular, as it can be applied early in the development
process and does not endanger any real-world operators. However, not all
safety-critical flaws become immediately observable in simulation. Some may
only become observable under certain critical conditions. If these conditions
are not covered, safety flaws may remain undetected. Creating critical tests is
therefore crucial. In recent years, there has been a trend towards using
Reinforcement Learning (RL) for this purpose. Guided by domain-specific reward
functions, RL algorithms are used to learn critical test strategies. This paper
presents a case study in which the collision avoidance behavior of a mobile
robot is subjected to RL-based testing. The study confirms prior research which
shows that RL can be an effective testing tool. However, the study also
highlights certain challenges associated with RL-based testing, namely (i) a
possible lack of diversity in test conditions and (ii) the phenomenon of reward
hacking where the RL agent behaves in undesired ways due to a misalignment of
reward and test specification. The challenges are illustrated with data and
examples from the experiments, and possible mitigation strategies are
discussed.
