Itinerary planning via deep reinforcement learning

Abstract

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.