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User: nikolai-kivel

Worrisome Properties of Neural Network Controllers and Their Symbolic Representations. (arXiv:2307.15456v1 [cs.LG])

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

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