Intheoriginalformulation, observations are assumed to be exact (non-noisy), so the GP likelihood only included a small observation noise σ2obs for numerical stability [32].

In a seminal paper, Szegedy et al. [Sze+14] observed that deep networks are extremely vulnerable to adversarial examples, namely, very small perturbations to the inputs

In a seminal paper, Szegedy et al. [Sze+14] observed that deep networks are extremely vulnerable to adversarial examples, namely, very small perturbations to the inputs

Deep reinforcement learning (RL) algorithms have demonstrated promising results on a variety of complex tasks, such as robotic manipulation [22, 13] and strategy games [27, 38].