@misc{indiciae621d73d85094,
  title = {Reinforcement Learning-Based Oscillation Dampening: Scaling Up Single-Agent Reinforcement Learning Algorithms to a 100-Autonomous-Vehicle Highway Field Operational Test},
  author = {Jang, Kathy [University of California, Berkeley, CA (United States); Waymo, San Francisco, CA (United States); University of California Berkeley] (ORCID:0009000695295535) and Lichtlé, Nathan [University of California, Berkeley, CA (United States)] (ORCID:0000000255216517) and Vinitsky, Eugene [New York University, NY (United States)] and Shah, Adit [University of California, Berkeley, CA (United States)] (ORCID:000000020586249X) and Bunting, Matthew [Vanderbilt University, Nashville, TN (United States)] and Nice, Matthew [Vanderbilt University, Nashville, TN (United States)] (ORCID:0000000336091760) and Piccoli, Benedetto [Rutgers University-Camden, NJ (United States)] (ORCID:0000000260005394) and Seibold, Benjamin [Temple University, Philadelphia, PA (United States)] (ORCID:0000000328796402) and Work, Daniel B. [Vanderbilt University, Nashville, TN (United States)] and Delle Monache, Maria Laura [University of California, Berkeley, CA (United States)] and Sprinkle, Jonathan [Vanderbilt University, Nashville, TN (United States)] and Lee, Jonathan W. [University of California, Berkeley, CA (United States)] and Bayen, Alexandre M. [University of California, Berkeley, CA (United States)]},
  year = {2025},
  doi = {10.1109/mcs.2024.3503372},
  url = {https://www.osti.gov/biblio/2545915},
  note = {Source identifier: 2545915}
}