Deep　architecture　can　improve　performance　of　neural　networks　whereas　it　increases　the　computational　complexity.　Compressing　networks　is　the　key　to　solve　this　problem.　The　framework　Knowledge　Distilling　(KD)　compresses　cumbersome　networks　well.　It　improved　mimic　learning,　enabling　knowledge　to　be　transferred　from　cumbersome　networks　to　compressed　networks　without　constraint　of　architectures.　Inspired　by　AlphaGo　Zero,　this　paper　proposed　an　algorithm　combining　KD　with　reinforcement　learning　to　compress　networks　on　changing　datasets.　In　this　algorithm,　the　compressed　networks　interact　with　the　environment　made　by　KD　to　produce　datasets　that　are　appropriate　w.r.t　the　model.　Monte　Carlo　Tree　Search　(MCTS)　of　AlphaGo　Zero　is　used　to　produce　the　datasets　by　making　a　trade-off　between　the　prediction　of　compressed　networks　and　the　knowledge.　In　experiments,　the　algorithm　proved　to　be　effective　in　compressing　networks　by　training　ResNet　on　CIFAR　datasets,　with　mean　squared　error　as　the　object　function.　©　2018　IEEE.