Dynamic　adaptive　streaming　over　HTTP　(DASH)　is　the　dominant　technology　of　multimedia　delivery　over　the　Internet.　In　DASH　system,　adaptive　bitrate　(ABR)　algorithms　running　on　client-side　video　player　are　the　key　to　improve　user　quality　of　experience　(QoE).　However,　most　existing　ABR　algorithms　employ　fixed　control　rules　to　make　bitrate　decisions　based　on　throughput,　playback　buffer　size,　or　a　combination　of　the　two.　As　a　result,　their　performance　in　the　complicated　and　fluctuant　network　environment　is　incompetent.　In　this　paper,　we　propose　QRL,　a　bitrate　adaptation　approach　based　on　deep　reinforcement　learning.　QRL　uses　double　Q-Learning,　an　enhanced　Q-Learning　method.　After　training　the　neural　network　model,　the　algorithm　can　select　proper　bitrates　for　future　video　segments　based　on　all　the　information　collected　by　client　during　the　video　playback　process.　Simulation　results　show　that　QRL　achieves　better　performance　than　other　algorithms.　©　2019　IOP　Publishing　Ltd.　All　rights　reserved.