  Deep Neural Network-based systems are now the state-of-the-art in many
robotics tasks, but their application in safety-critical domains remains
dangerous without formal guarantees on network robustness. Small perturbations
to sensor inputs (from noise or adversarial examples) are often enough to
change network-based decisions, which was already shown to cause an autonomous
vehicle to swerve into oncoming traffic. In light of these dangers, numerous
algorithms have been developed as defensive mechanisms from these adversarial
inputs, some of which provide formal robustness guarantees or certificates.
This work leverages research on certified adversarial robustness to develop an
online certified defense for deep reinforcement learning algorithms. The
proposed defense computes guaranteed lower bounds on state-action values during
execution to identify and choose the optimal action under a worst-case
deviation in input space due to possible adversaries or noise. The approach is
demonstrated on a Deep Q-Network policy and is shown to increase robustness to
noise and adversaries in pedestrian collision avoidance scenarios and a classic
control task.
