The University of Arizona
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Multi-agent reinforcement learning for optimization of mixed autonomy traffic at scale

Modeling, Computation, Nonlinearity, Randomness and Waves Seminar

Multi-agent reinforcement learning for optimization of mixed autonomy traffic at scale
Series: Modeling, Computation, Nonlinearity, Randomness and Waves Seminar
Location: ONLINE
Presenter: Eugene Vinitsky, Dept of Mechanical Engineering, UC Berkeley

While the promised appearance of fully autonomous vehicles has been pushed back further and further, our highways have silently been transformed by the increasing penetration of hands-off adaptive cruise controllers. We investigate how, given current levels of cruise control availability, we can design driving strategies for these cruise controllers that increase the energy efficiency of the highway by smoothing out spontaneously forming shockwaves. Using multi-agent reinforcement learning, we show that we can design controllers that approximately act like they know the equilibrium speed of the system. These controllers outperform hand-designed control strategies and are robust to variations of the underlying dynamics.

Zoom:  https://arizona.zoom.us/j/94534134312   
Password:  “arizona” (all lower case)