  Evaluation of deep reinforcement learning (RL) is inherently challenging.
Especially the opaqueness of learned policies and the stochastic nature of both
agents and environments make testing the behavior of deep RL agents difficult.
We present a search-based testing framework that enables a wide range of novel
analysis capabilities for evaluating the safety and performance of deep RL
agents. For safety testing, our framework utilizes a search algorithm that
searches for a reference trace that solves the RL task. The backtracking states
of the search, called boundary states, pose safety-critical situations. We
create safety test-suites that evaluate how well the RL agent escapes
safety-critical situations near these boundary states. For robust performance
testing, we create a diverse set of traces via fuzz testing. These fuzz traces
are used to bring the agent into a wide variety of potentially unknown states
from which the average performance of the agent is compared to the average
performance of the fuzz traces. We apply our search-based testing approach on
RL for Nintendo's Super Mario Bros.
