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Reinforcement learning for mixed autonomy mobility

Cathy Wu (UC Berkeley)
4:50pm-5:30pm Thursday, September 6, 2018
Implementing AI
 Location: Imperial B
Secondary topics:  Reinforcement Learning, Transportation and Logistics

What you'll learn

  • Explore techniques in machine learning and optimization critical for enabling mixed-autonomy mobility, particularly for the automated vehicle use case

    Description

    Self-driving cars are not expected to reach full adoption for at least another 35 years. So in the meantime, how will self-driving cars change urban mobility?

    Cathy Wu shares techniques in machine learning and optimization critical for enabling mixed-autonomy mobility, the gradual and complex integration of automated vehicles into the existing transportation system. Using novel techniques in model-free deep reinforcement learning and control theory, Cathy explores and quantifies the potential impact of a small fraction of automated vehicles on low-level traffic flow dynamics, such as congestion on a variety of important traffic contexts.

    Photo of Cathy Wu

    Cathy Wu

    UC Berkeley

    Cathy Wu works at the intersection of machine learning, optimization, and large-scale societal systems. Her recent research focuses on mixed autonomy systems in mobility, which studies the complex integration of automation such as self-driving cars into existing urban systems. She is interested in developing computational tools for reliable and complex decision making in critical societal systems. Cathy will be joining MIT as an assistant professor in 2019. Cathy holds a PhD in EECS from UC Berkeley, where she was part of the Berkeley AI Research lab, DeepDrive, California PATH, and RISELab, and a BS and MEng in EECS from MIT.