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 *Nick Rhinehart* from the University of California, Berkeley to talk about "Jointly Forecasting and Controlling Behavior by Learning From High-Dimensional Data".
Nick Rhinehart is a Postdoctoral Scholar in the Electrical Engineering and Computer Science Department at the University of California, Berkeley with Sergey Levine. His work focuses on fundamental and applied research in machine learning and computer vision for behavioral forecasting and control in complex environments, with an emphasis on imitation learning, reinforcement learning, and deep learning methods. Applications of his work include autonomous navigation, robotic manipulation, and first-person video. He received a Ph.D. in Robotics from Carnegie Mellon University with Kris Kitani, and B.S. and B.A. degrees in Engineering and Computer Science from Swarthmore College. Nick's work has been honored with a Best Paper Award at the ICML 2019 Workshop on AI for Autonomous Driving and a Best Paper Honorable Mention Award at ICCV 2017. His work has been published at a variety of top-tier venues in machine learning, computer vision, and robotics, including AAMAS, CoRL, CVPR, ECCV, ICCV, ICLR, ICML, ICRA, NeurIPS, and PAMI. You can learn more about his work at https://people.eecs.berkeley.edu/~nrhinehart/.
The seminar will be on *Thursday, December 3rd, 2020 5:00 PM PT / 7:00 PM CT / 8:00 PM ET / Friday, December 4th, 9:00 AM Beijing*. Details about the talk can be found on our website <https://arlseminar.com>.
You can get access to the seminar by 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.
Best,
ARL Seminar
*Title: Jointly Forecasting and Controlling Behavior by Learning From High-Dimensional Data
*Abstract: A primary goal of many scientific and engineering disciplines is to develop accurate predictive models. Predictive models are also critical to human intelligence, as they enable us to plan behaviors by reasoning about how actions affect the world around us. These models are especially useful when they can accurately predict the future behaviors of other agents, which enables planning in their presence. In this talk, I will describe some of my research on developing learning-based models to jointly perform forecasting, planning, and control in a unified framework that draws inspiration from concepts in Imitation Learning and Reinforcement Learning. I will show how these models can be learned to make accurate predictions and decisions in the presence of rich perceptual input, and demonstrate their application to single- and multi-agent settings in first-person video, robotic manipulation, and autonomous navigation.
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Zhiwei Qin
Principal Research Scientist, Director of AI Research
DiDi Research America, LLC
Mountain View CA
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