112 citations · 118 across the 5 of their papers we have counts for
5 papers
Improving the generalizability and robustness of large-scale traffic signal control
Tianyu Shi, Francois-Xavier Devailly, Denis Larocque +1
A number of deep reinforcement-learning (RL) approaches propose to control traffic signals. In this work, we study the robustness of such methods along two axes. First, sensor fail…
Model-based graph reinforcement learning for inductive traffic signal control
François-Xavier Devailly, Denis Larocque, Laurent Charlin
Most reinforcement learning methods for adaptive-traffic-signal-control require training from scratch to be applied on any new intersection or after any modification to the road ne…
RCC-GAN: Regularized Compound Conditional GAN for Large-Scale Tabular Data Synthesis
Mohammad Esmaeilpour, Nourhene Chaalia, Adel Abusitta +3
This paper introduces a novel generative adversarial network (GAN) for synthesizing large-scale tabular databases which contain various features such as continuous, discrete, and b…
Bi-Discriminator Class-Conditional Tabular GAN
Mohammad Esmaeilpour, Nourhene Chaalia, Adel Abusitta +3
This paper introduces a bi-discriminator GAN for synthesizing tabular datasets containing continuous, binary, and discrete columns. Our proposed approach employs an adapted preproc…
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control
François-Xavier Devailly, Denis Larocque, Laurent Charlin
Scaling adaptive traffic-signal control involves dealing with combinatorial state and action spaces. Multi-agent reinforcement learning attempts to address this challenge by distri…