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We raise concerns about controllers' robustness in simple reinforcementlearning benchmark problems. We focus on neural network controllers and theirlow neuron and symbolic abstractions. A typical controller reaching high meanreturn values still generates an abundance of persistent low-return solutions,which is a highly undesirable property, easily exploitable by an adversary. Wefind that the simpler controllers admit more persistent bad solutions. Weprovide an algorithm for a systematic robustness study and prove existence ofpersistent solutions and, in some cases, periodic orbits, using acomputer-assisted proof methodology.

Worrisome Properties of Neural Network Controllers and Their Symbolic Representations