Routing an Autonomous Taxi with Reinforcement Learning

Miyoung Han (1, 2) , Pierre Senellart (2, 1) , Stéphane Bressan (2) , Huayu Wu (3)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 IPAL - Image & Pervasive Access Lab
3 A*STAR - Agency for science, technology and research [Singapore]

Singapore's vision of a Smart Nation encompasses the development of effective and efficient means of transportation. The government's target is to leverage new technologies to create services for a demand-driven intelligent transportation model including personal vehicles, public transport, and taxis. Singapore's government is strongly encouraging and supporting research and development of technologies for autonomous vehicles in general and autonomous taxis in particular. The design and implementation of intelligent routing algorithms is one of the keys to the deployment of autonomous taxis. In this paper we demonstrate that a reinforcement learning algorithm of the Q-learning family, based on a customized exploration and exploitation strategy, is able to learn optimal actions for the routing autonomous taxis in a real scenario at the scale of the city of Singapore with pick-up and drop-off events for a fleet of one thousand taxis.

Mots clés: dissemin

Domaines: Informatique [cs]

Miyoung Han, Pierre Senellart, Stéphane Bressan, Huayu Wu. Routing an Autonomous Taxi with Reinforcement Learning. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16, 2016, -, France. ⟨10.1145/2983323.2983379⟩. ⟨hal-02143465⟩