With　the　rapid　development　of　air　transportation　in　recent　years,　airport　operations　have　attracted　a　lot　of　attention.　Among　them,　airport　gate　assignment　problem　(AGAP)　has　become　a　research　hotspot.　However,　the　real-time　AGAP　algorithm　is　still　an　open　issue.　In　this　study,　a　deep　reinforcement　learning　based　AGAP(DRL-AGAP)　is　proposed.　The　optimization　object　is　to　maximize　the　rate　of　flights　assigned　to　fixed　gates.　The　real-time　AGAP　is　modeled　as　a　Markov　decision　process　(MDP).　The　state　space,　action　space,　value　and　rewards　have　been　defined.　The　DRL-AGAP　algorithm　is　evaluated　via　simulation　and　it　is　compared　with　the　flight　pre-assignment　results　of　the　optimization　software　Gurobiand　Greedy.　Simulation　results　show　that　the　performance　of　the　proposed　DRL-AGAP　algorithm　is　close　to　that　of　pre-assignment　obtained　by　the　Gurobi　optimization　solver.　Meanwhile,　the　real-time　assignment　ability　is　ensured　by　the　proposed　DRL-AGAP　algorithm　due　to　the　dynamic　modeling　and　lower　complexity.　Copyright　©　by　HIGH　TECHNOLOGY　LETTERS　PRESS.