Title: Lagrangian Control at Large and Local Scales in Mixed Autonomy Traffic Flow

Abstract: This talk investigates Lagrangian (mobile) control of traffic flow at local scale (vehicular level), and how self-driving vehicles will change traffic flow patterns. Professor Bayen describes approaches based on deep, reinforcement learning presented in the context of enabling mixed-autonomy mobility. This lecture also explores the gradual and complex integration of automated vehicles into the existing traffic system. Attendees will learn the potential impact of a small fraction of automated vehicles on low-level traffic flow dynamics, using novel techniques in model-free, deep reinforcement learning, in which the automated vehicles act as mobile (Lagrangian) controllers to traffic flow.

Illustrative examples will be presented in the context of a new, open-source computational platform called FLOW, which integrates state-of-the-art microsimulation tools with deep-RL libraries on AWS EC2. Interesting behavior of mixed autonomy traffic will be revealed in the context of emergent behavior of traffic: https://flow-project.github.io/