  Learning optimal control policies directly on physical systems is challenging
since even a single failure can lead to costly hardware damage. Most existing
model-free learning methods that guarantee safety, i.e., no failures, during
exploration are limited to local optima. A notable exception is the GoSafe
algorithm, which, unfortunately, cannot handle high-dimensional systems and
hence cannot be applied to most real-world dynamical systems. This work
proposes GoSafeOpt as the first algorithm that can safely discover globally
optimal policies for high-dimensional systems while giving safety and
optimality guarantees. We demonstrate the superiority of GoSafeOpt over
competing model-free safe learning methods on a robot arm that would be
prohibitive for GoSafe.
