27 citations · 63 across the 6 of their papers we have counts for
6 papers
Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs
Jonathan W. Lee, Han Wang, Kathy Jang +61
The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,…
Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test
Kathy Jang, Nathan Lichtlé, Eugene Vinitsky +10
In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smoot…
Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data
Nathan Lichtlé, Kathy Jang, Adit Shah +3
Designing traffic-smoothing cruise controllers that can be deployed onto autonomous vehicles is a key step towards improving traffic flow, reducing congestion, and enhancing fuel e…
Robust Reinforcement Learning using Adversarial Populations
Eugene Vinitsky, Yuqing Du, Kanaad Parvate +3
Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are…
Zero-Shot Autonomous Vehicle Policy Transfer: From Simulation to Real-World via Adversarial Learning
Behdad Chalaki, Logan E. Beaver, Ben Remer +4
In this article, we demonstrate a zero-shot transfer of an autonomous driving policy from simulation to University of Delaware's scaled smart city with adversarial multi-agent rein…
Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehicles
Kathy Jang, Eugene Vinitsky, Behdad Chalaki +4
Using deep reinforcement learning, we train control policies for autonomous vehicles leading a platoon of vehicles onto a roundabout. Using Flow, a library for deep reinforcement l…