  While reinforcement learning produces very promising results for many
applications, its main disadvantage is the lack of safety guarantees, which
prevents its use in safety-critical systems. In this work, we address this
issue by a safety shield for nonlinear continuous systems that solve
reach-avoid tasks. Our safety shield prevents applying potentially unsafe
actions from a reinforcement learning agent by projecting the proposed action
to the closest safe action. This approach is called action projection and is
implemented via mixed-integer optimization. The safety constraints for action
projection are obtained by applying parameterized reachability analysis using
polynomial zonotopes, which enables to accurately capture the nonlinear effects
of the actions on the system. In contrast to other state-of-the-art approaches
for action projection, our safety shield can efficiently handle input
constraints and dynamic obstacles, eases incorporation of the spatial robot
dimensions into the safety constraints, guarantees robust safety despite
process noise and measurement errors, and is well suited for high-dimensional
systems, as we demonstrate on several challenging benchmark systems.
