Testing　phase　augmentation　is　a　fast　way　to　further　improve　the　performance　of　image　classification　when　CNN　(Convolutional　Neural　Network)　is　already　trained　for　hours.　Limited　attempts　have　been　made　to　find　the　best　augmentation　strategy　for　testing　set.　We　propose　a　reinforcement　learning　based　augmentation　strategy　searching　method　for　testing　phase　augmentation.　With　the　augmentation　strategy,　we　augment　each　testing　image　and　integrate　features　of　its　augmented　images　into　one　feature.　The　reinforcement　learning　method　searches　the　best　parameters　in　the　augmentation　strategy　which　is　formed　as　a　matrix　in　this　paper.　Using　the　proposed　method,　we　achieve　competitive　accuracies　on　image　classification　and　face　verification.　©　Springer　Nature　Switzerland　AG　2019.