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.