Deep Reinforcement Learning with Applications in Transportation

This tutorial aims to provide the audience with a guided introduction to deep reinforcement learning (DRL) with specially curated application case studies in transportation. The tutorial covers both theory and practice, with more emphasis on the practical aspects of DRL that are pertinent to tackle transportation challenges. Some core examples include online ride order dispatching, fleet management, traffic signals control, route planning, and autonomous driving.