Recent research on edge-preserving image smoothing has suggested that bilateral filtering is vulnerable to maliciously perturbed filtering input. However, while most prior works analyze the adaptation of the range kernel in one-step manner, in this paper we take a more constructive view towards multi-step framework with the goal of unveiling the vulnerability of bilateral filtering. To this end, we adaptively model the width setting of range kernel as a multi-agent reinforcement learning problem and learn an adaptive actor-critic bilateral filter from local image context during successive bilateral filtering operations. By evaluating on eight benchmark datasets, we show that the performance of our filter outperforms that of state-of-the-art bilateral-filtering methods in terms of both salient structures preservation and insignificant textures and perturbation elimination.