Automatic　itinerary　planning　that　provides　an　epic　journey　for　each　traveler　is　a　fundamental　yet　inefficient　task.　Most　existing　planning　methods　apply　heuristic　guidelines　for　certain　objective,　and　thereby　favor　popular　preferred　point　of　interests　(POIs)　with　high　probability,　which　ignore　the　intrinsic　correlation　between　the　POIs　exploration,　traveler＇s　preferences,　and　distinctive　attractions.　To　tackle　the　itinerary　planning　problem,　this　paper　explores　the　connections　of　these　three　objectives　in　probabilistic　manner　based　on　a　Bayesian　model　and　proposes　a　triple-agent　deep　reinforcement　learning　approach,　which　generates　4-way　direction,　4-way　distance,　and　3-way　selection　strategy　for　iteratively　determining　next　POI　to　visit　in　the　itinerary.　Experiments　on　five　real-world　cities　demonstrate　that　our　triple-agent　deep　reinforcement　learning　approach　can　provide　better　planning　results　in　comparison　with　state-of-the-art　multiobjective　optimization　methods.