9 citations · 21 across the 6 of their papers we have counts for
6 papers
Supervised and Unsupervised Neural Network Solver for First Order Hyperbolic Nonlinear PDEs
Zakaria Baba, Alexandre M. Bayen, Alexi Canesse +7
We present a neural network-based method for learning scalar hyperbolic conservation laws. Our method replaces the traditional numerical flux in finite volume schemes with a traina…
(U)NFV: Supervised and Unsupervised Neural Finite Volume Methods for Solving Hyperbolic PDEs
Nathan Lichtlé, Alexi Canesse, Zhe Fu +3
We introduce (U)NFV, a modular neural network architecture that generalizes classical finite volume (FV) methods for solving hyperbolic conservation laws. Hyperbolic partial differ…
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…
Reinforcement Learning in Control Theory: A New Approach to Mathematical Problem Solving
Kala Agbo Bidi, Jean-Michel Coron, Amaury Hayat +1
One of the central questions in control theory is achieving stability through feedback control. This paper introduces a novel approach that combines Reinforcement Learning (RL) wit…
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world
Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang +2
We introduce Nocturne, a new 2D driving simulator for investigating multi-agent coordination under partial observability. The focus of Nocturne is to enable research into inference…