  Deep Reinforcement Learning (DRL) algorithms have been increasingly employed
during the last decade to solve various decision-making problems such as
autonomous driving and robotics. However, these algorithms have faced great
challenges when deployed in safety-critical environments since they often
exhibit erroneous behaviors that can lead to potentially critical errors. One
way to assess the safety of DRL agents is to test them to detect possible
faults leading to critical failures during their execution. This raises the
question of how we can efficiently test DRL policies to ensure their
correctness and adherence to safety requirements. Most existing works on
testing DRL agents use adversarial attacks that perturb states or actions of
the agent. However, such attacks often lead to unrealistic states of the
environment. Their main goal is to test the robustness of DRL agents rather
than testing the compliance of agents' policies with respect to requirements.
Due to the huge state space of DRL environments, the high cost of test
execution, and the black-box nature of DRL algorithms, the exhaustive testing
of DRL agents is impossible. In this paper, we propose a Search-based Testing
Approach of Reinforcement Learning Agents (STARLA) to test the policy of a DRL
agent by effectively searching for failing executions of the agent within a
limited testing budget. We use machine learning models and a dedicated genetic
algorithm to narrow the search towards faulty episodes. We apply STARLA on
Deep-Q-Learning agents which are widely used as benchmarks and show that it
significantly outperforms Random Testing by detecting more faults related to
the agent's policy. We also investigate how to extract rules that characterize
faulty episodes of the DRL agent using our search results. Such rules can be
used to understand the conditions under which the agent fails and thus assess
its deployment risks.
