Itinerary　planning　that　provides　tailor-made　tours　for　each　traveler　is　a　fundamental　yet　inefficient　task　in　route　recommendation.　In　this　paper,　we　propose　an　automatic　route　recommendation　approach　with　deep　reinforcement　learning　to　solve　the　itinerary　planning　problem.　We　formulate　automatic　generation　of　route　recommendation　as　Markov　Decision　Process　(MDP)　and　then　solve　it　by　our　variational　agent　optimized　through　deep　Q-learning　algorithm.　We　train　our　agent　using　open　data　over　various　cities　and　show　that　the　agent　accomplishes　notable　improvement　in　comparison　with　other　state-of-the-art　methods.　©　2020　ACM.