The　past　few　years　have　witnessed　a　wide　deployment　of　software　defined　networks　facilitating　a　separation　of　the　control　plane　from　the　forwarding　plane.　However,　the　work　on　the　control　plane　largely　relies　on　a　manual　process　in　configuring　forwarding　strategies.　To　address　this　issue,　this　paper　presents　NetworkAI,　an　intelligent　architecture　for　self-learning　control　strategies　in　software　defined　networking　networks.　NetworkAI　employs　deep　reinforcement　learning　and　incorporates　network　monitoring　technologies,　such　as　the　in-band　network　telemetry　to　dynamically　generate　control　policies　and　produces　a　near　optimal　decision.　Simulation　results　demonstrated　the　effectiveness　of　NetworkAI.