Learning　to　navigate　in　an　unknown　environment　is　a　crucial　capability　of　mobile　robot.　Conventional　method　for　robot　navigation　consists　of　three　steps,　involving　localization,　map　building　and　path　planning.　However,　most　of　the　conventional　navigation　methods　rely　on　obstacle　map,　and　dont　have　the　ability　of　autonomous　learning.　In　contrast　to　the　traditional　approach,　we　propose　an　end-to-end　approach　in　this　paper　using　deep　reinforcement　learning　for　the　navigation　of　mobile　robots　in　an　unknown　environment.　Based　on　dueling　network　architectures　for　deep　reinforcement　learning　(Dueling　DQN)　and　deep　reinforcement　learning　with　double　q　learning　(Double　DQN),　a　dueling　architecture　based　double　deep　q　network　(D3QN)　is　adapted　in　this　paper.　Through　D3QN　algorithm,　mobile　robot　can　learn　the　environment　knowledge　gradually　through　its　wonder　and　learn　to　navigate　to　the　target　destination　autonomous　with　an　RGB-D　camera　only.　The　experiment　results　show　that　mobile　robot　can　reach　to　the　desired　targets　without　colliding　with　any　obstacles.　©　2019　IEEE.