INFORMS Open Forum

Applied Reinforcement Learning (ARL) Seminar - Oct. 21, 2020 8:00 AM PT / 10:00 AM CT / 11:00 AM ET

  • 1.  Applied Reinforcement Learning (ARL) Seminar - Oct. 21, 2020 8:00 AM PT / 10:00 AM CT / 11:00 AM ET

    Posted 10-20-2020 17:11
    Edited by Zhiwei (Tony) Qin 10-20-2020 17:15
    Dear colleagues,

    The Applied Reinforcement Learning (ARL) Seminar is an online seminar that presents the latest advances in reinforcement learning applications and theory, organized by Drs. Rui Song, Hongtu Zhu, Tony Qin, Jieping Ye and Michael R. Kosorok.

     

    Reinforcement Learning (RL) learns how the agents should take actions when interacting with the environment to obtain the highest reward. Due to many successful applications in robotics, games, precision health, e-commerce and ride-sharing industries, Reinforcement Learning (RL) has gained great popularity among various scientific fields. The goal of the ARL Seminar is to bring you a virtual seminar (approximately) featuring the latest work in applying reinforcement learning methods in many exciting areas (e.g., health sciences, or two-sided markets). 

     

    This time, the ARL Seminar is excited to welcome KDD Cup 2020 RL Track Winners *Yansheng Wang* and *Fanyou Wu* to present their champion solutions and some understandings of applied reinforcement learning to the order dispatch and vehicle reposition task on the Mobility-on-Demand Platform. 

     

    Yansheng Wang is currently a first year Ph.D. candidate in the School of Computer Science and Engineering at Beihang University. He is working on crowd intelligence, spatial crowdsourcing and reinforcement learning. He has published several papers in highly refereed conferences and journals such as ICDE, AAAI and Neurocomputing. He is the team leader of the champion team in KDD CUP 2020 RL track.  

     

    Fanyou Wu is now a Ph.D. candidate in the Forestry and Natural Resources Department, Purdue University. His research focuses on the application of machine learning in forestry and transportation, and has published several papers in those fields. He has also won many championships and runners-up in machine learning related competitions, including the title of JDD (2019), the tournament of IJCAI Adversarial AI Challenge (2019), and champion of KDD Cup (2020).

     

    The seminar will be on *Wednesday, October 21st, 2020* at *8:00 AM PT / 10:00 AM CT / 11:00 AM ET / 4:00 PM London / 11:00 PM Beijing*. Details about the talk can be found on our website <https://arlseminar.com>.

     

    You can get access to the seminar by Zoom or YouTube Live in the USA or Bilibili Live in China.

    YouTube Live Channel: https://www.youtube.com/channel/UCYtw_0jwqtNW0-6NFsPY9BA/live

    Bilibili Live: https://live.bilibili.com/22533038

     

    If you are interested in getting updates of our seminar's events in the future, you can register on our website <https://www.arlseminar.com/registration-form/>. We will notify you of the new event at the first time.

     

    We look forward to seeing you on Wednesday, Oct 21st.

     

    Best,

    ARL Seminar

     

    *Title: KDD Cup 2020 RL Track Winners Presentation

    *Abstract1 (Yansheng Wang): The development of the sharing economy and mobile Internet has stimulated an explosion of real-world dynamic ridesharing applications. Among them the order dispatching is vital to ridesharing platforms. Given dynamic input of orders and available drivers, order dispatching aims to assign drivers to suitable orders with the objective of maximizing the overall platform revenue. In this talk, I will introduce two types of reinforcement learning (RL) based approaches to solve the problem. First, I will present an adaptive batch-based approach, where RL is applied to decide the batch sizes. Then I will elaborate on another fixed batch-based approach, where we use RL to guide the in-batch matching decisions, which is also the champion solution of order dispatching tasks in KDD CUP 2020 RL track. Finally, I will highlight some other research challenges in RL-based order dispatching in the future.

    *Abstract2 ( Fanyou Wu): Machine Learning competitions are often considered as bridges between industries and researches. Leading top solutions have often become state-of-the-art methods in the real world. In this presentation, I want to share some experiences about those competitions and use the vehicle dispatching task in the KDD RL track as the main example. The vehicle dispatching system has always been one of the most critical problems in online taxi-hailing platforms to adapt the operation and management strategy to demand and supply dynamics. In the KDD competition, my team used a single agent deep reinforcement learning approach for vehicle repositioning by deploying idle vehicles to specific locations to anticipate future demand at the destination. A global pruned action space, which encompasses a set of discrete actions, is used in this approach. It can benefit drivers by avoiding traveling to distant outskirts where there are few order requests. In addition, my team designed a simulator using the Julia programming language, which brings about over ten times optimization in speed compared with the Python simulator implementation.



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    Zhiwei Qin
    Principal Research Scientist, Director of AI Research
    DiDi Research America, LLC
    Mountain View CA
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