Crowd　counting　is　still　a　challenging　task　in　crowded　scenario　due　to　heavy　occlusions,　appearance　variations　and　perspective　distortions.　Thanks　to　the　development　of　deep　convolutional　neural　networks,　especially　the　deformable　convo-lution　neurral　networks,　great　progress　has　been　made　for　crowd　counting.　However,　existing　deformable　convolution　networks　cannot　get　satisfactory　performance　due　to　limited　receptive　field　that　is　not　conducive　to　crowded　object　detection.　In　this　paper,　a　novel　end-to-end　Reinforced-Deformable　convolutional　neural　Network　(RDNet)　is　proposed.　First,　a　multi-scale　context　information　fusion　strategy　with　skipping　connection　and　shortcut　connection　is　utilized　to　obtain　rich　semantic　features　and　local　details　of　the　crowd　distribution.　Then　a　reinforced-deformable　convolution　module　is　designed　as　the　back-end　of　the　network,　which　can　sample　with　adaptive　offsets　and　scalars　to　enlarge　the　receptive　field　for　more　accurate　edge　information　and　crowd　distribution　localization.　Finally,　an　impulse　function　convolved　with　cuphead　Gaussian　kernel　is　applied　to　generate　density　map　for　counting.　Extensive　experimental　results　on　four　popular　datasets,　namely,　Shangha-itech,　UCF　-CC-50,　WorldEXPO＇10,　and　UCF-QNRF,　show　that　our　proposed　approach　outperforms　other　state-of-the-art　methods.　©　2022　IEEE.