  Recently, reinforcement learning (RL) has been used as a tool for finding
failures in autonomous systems. During execution, the RL agents often rely on
some domain-specific heuristic reward to guide them towards finding failures,
but constructing such a heuristic may be difficult or infeasible. Without a
heuristic, the agent may only receive rewards at the time of failure, or even
rewards that guide it away from failures. For example, some approaches give
rewards for taking more-likely actions, because we want to find more-likely
failures. However, the agent may then learn to only take likely actions, and
may not be able to find a failure at all. Consequently, the problem becomes a
hard-exploration problem, where rewards do not aid exploration. A new
algorithm, go-explore (GE), has recently set new records on benchmarks from the
hard-exploration field. We apply GE to adaptive stress testing (AST), one
example of an RL-based falsification approach that provides a way to search for
the most-likely failure scenario. We simulate a scenario where an autonomous
vehicle drives while a pedestrian is crossing the road. We demonstrate that GE
is able to find failures without domain-specific heuristics, such as the
distance between the car and the pedestrian, on scenarios that other RL
techniques are unable to solve. Furthermore, inspired by the robustification
phase of GE, we demonstrate that the backwards algorithm (BA) improves the
failures found by other RL techniques.
