Handwritten　digits　recognition　is　a　challenging　problem　in　recent　years.　Although　many　deep　learning-based　classification　algorithms　are　studied　for　handwritten　digits　recognition,　the　recognition　accuracy　and　running　time　still　need　to　be　further　improved.　In　this　paper,　an　adaptive　deep　Q-learning　strategy　is　proposed　to　improve　accuracy　and　shorten　running　time　for　handwritten　digit　recognition.　The　adaptive　deep　Q-learning　strategy　combines　the　feature-extracting　capability　of　deep　learning　and　the　decision-making　of　reinforcement　learning　to　form　an　adaptive　Q-learning　deep　belief　network　(Q-ADBN).　First,　Q-ADBN　extracts　the　features　of　original　images　using　an　adaptive　deep　auto-encoder　(ADAE),　and　the　extracted　features　are　considered　as　the　current　states　of　Q-learning　algorithm.　Second,　Q-ADBN　receives　Q-function　(reward　signal)　during　recognition　of　the　current　states,　and　the　final　handwritten　digits　recognition　is　implemented　by　maximizing　the　Q-function　using　Q-learning　algorithm.　Finally,　experimental　results　from　the　well-known　MNIST　dataset　show　that　the　proposed　Q-ADBN　has　a　superiority　to　other　similar　methods　in　terms　of　accuracy　and　running　time.　(C)　2018　Elsevier　Ltd.　All　rights　reserved.