  Safety validation is important during the development of safety-critical
autonomous systems but can require significant computational effort. Existing
algorithms often start from scratch each time the system under test changes. We
apply transfer learning to improve the efficiency of reinforcement learning
based safety validation algorithms when applied to related systems. Knowledge
from previous safety validation tasks is encoded through the action value
function and transferred to future tasks with a learned set of attention
weights. Including a learned state and action value transformation for each
source task can improve performance even when systems have substantially
different failure modes. We conduct experiments on safety validation tasks in
gridworld and autonomous driving scenarios. We show that transfer learning can
improve the initial and final performance of validation algorithms and reduce
the number of training steps.
