November 4-7, 2018
Maui, Hawaii, USA
Sponsored by the IEEE Intelligent Transportation Systems Society
Prof. Mykel Kochenderfer
Stanford University
Prof. Dan Work
Vanderbilt University
Dr. Guni Sharon
The University of Texas at Austin
Recent years have seen a steady stream of reinforcement learning (RL) highlights, as tremendous progress has been made in control of complex dynamical systems, as RL has emerged as a highly promising framework for control. At the same time, RL has yet to push the boundaries of real-world domains and applications. As transportation is a highly rich, complex dynamical system itself, we as a community have an opportunity to explore and push the theory, methodology, and practice of RL for practical transportation problems. In this workshop, we ask: How can RL help build towards safer, more reliable, smarter, transportation systems? How can the structure inherent in transportation problems help overcome challenges in sample-efficiency, scaling, heterogeneity, data limitations, communication constraints, and other challenges in RL? The goal of this workshop is to bring together researchers and practitioners from transportation, reinforcement learning, and control to address core challenges in intelligent transportation systems.
If you have questions, the contact email is evinitsky@berkeley.edu