{"type":"ScholarlyArticle","title":"Deep Reinforcement Learning for Ride-sharing Dispatching and Repositioning","authors":[{"name":"Zhiwei (Tony) Qin","slug":"zhiwei-tony-qin","given":"Zhiwei (Tony)","family":"Qin","affiliation":"DiDi Research America , Mountain View , CA , USA; DiDi AI Labs , Beijing , China"},{"name":"Xiaocheng Tang","slug":"xiaocheng-tang","given":"Xiaocheng","family":"Tang","affiliation":"DiDi Research America , Mountain View , CA , USA; DiDi AI Labs , Beijing , China"},{"name":"Yan Jiao","slug":"yan-jiao","given":"Yan","family":"Jiao","affiliation":"DiDi Research America , Mountain View , CA , USA; DiDi AI Labs , Beijing , China"},{"name":"Fan Zhang","slug":"fan-zhang","given":"Fan","family":"Zhang","affiliation":"DiDi AI Labs , Beijing , China"},{"name":"Chenxi Wang","slug":"chenxi-wang","given":"Chenxi","family":"Wang","affiliation":"DiDi Research America , Mountain View , CA , USA; DiDi Research , Beijing , China"},{"name":"Qun (Tracy) Li","slug":"qun-tracy-li","given":"Qun (Tracy)","family":"Li","affiliation":"DiDi Research , Beijing , China"}],"abstract":"In this demo, we will present a simulation-based human-computer interaction of deep reinforcement learning in action on order dispatching and driver repositioning for ride-sharing.  Specifically, we will demonstrate through several specially designed domains how we use deep reinforcement learning to train agents (drivers) to have longer optimization horizon and to cooperate to achieve higher objective values collectively.","doi":"10.24963/ijcai.2019/958","url":"https://ijcai.org/proceedings/2019/958","pdf":"https://ijcai.org/proceedings/2019/0958.pdf","markdown":"https://ijcai.org/proceedings/2019/958.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/958","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Demos","track":"Demos","number":958,"pages":{"first":6566,"last":6568},"keywords":["Applications: Transport and logistics","AI: Machine Learning","AI: Multiagent Systems","AI: Planning and Scheduling"],"videos":[],"references":[{"position":1,"raw":"[Holler et al., 2018] John Holler, Zhiwei Qin, Xiaocheng Tang, Yan Jiao, Tianchen Jin, Satinder Singh, Chenxi Wang, and Jieping Ye. Deep q-learning approaches to dynamic multi-driver dispatching and repositioning. In NeurIPS 2018 Deep Reinforcement Learning Workshop, 2018.","title":"Deep q-learning approaches to dynamic multi-driver dispatching and repositioning","authors":"John Holler, Zhiwei Qin, Xiaocheng Tang, Yan Jiao, Tianchen Jin, Satinder Singh, Chenxi Wang, Jieping Ye","year":"2018","venue":"NeurIPS 2018 Deep Reinforcement Learning Workshop","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Wang et al., 2018] Zhaodong Wang, Zhiwei Qin, Xi- aocheng Tang, Jieping Ye, and Hongtu Zhu. Deep re- inforcement learning with knowledge transfer for online rides order dispatching. In 2018 IEEE International Con- ference on Data Mining (ICDM), pages 617-626, Nov 2018.","title":"Deep reinforcement learning with knowledge transfer for online rides order dispatching","authors":"Zhaodong Wang, Zhiwei Qin, Xiaocheng Tang, Jieping Ye, Hongtu Zhu","year":"2018","venue":"IEEE International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Xu et al., 2018] Zhe Xu, Zhixin Li, Qingwen Guan, Ding- shui Zhang, Qiang Li, Junxiao Nan, Chunyang Liu, Wei Bian, and Jieping Ye. Large-scale order dispatch in on- demand ride-hailing platforms: A learning and planning approach. In Proceedings of the 24th ACM SIGKDD In- ternational Conference on Knowledge Discovery & Data Mining, pages 905-913. ACM, 2018.","title":"Large-scale order dispatch in ondemand ride-hailing platforms: A learning and planning approach","authors":"Zhe Xu, Zhixin Li, Qingwen Guan, Dingshui Zhang, Qiang Li, Junxiao Nan, Chunyang Liu, Wei Bian, Jieping Ye","year":"2018","venue":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}