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Quantifying the Impact of Non-Stationarity in Reinforcement
  Learning-Based Traffic Signal Control

Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control

9 April 2020
L. N. Alegre
A. Bazzan
Bruno C. da Silva
ArXivPDFHTML

Papers citing "Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control"

3 / 3 papers shown
Title
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration
Amir Baghi
Jens Sjölund
Joakim Bergdahl
Linus Gisslén
Alessandro Sestini
58
0
0
17 Mar 2025
The Real Deal: A Review of Challenges and Opportunities in Moving
  Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality
The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality
Rex Chen
Fei Fang
Norman M. Sadeh
35
8
0
23 Jun 2022
Towards Real-World Deployment of Reinforcement Learning for Traffic
  Signal Control
Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control
Arthur Muller
Vishal S. Rangras
Georg Schnittker
Michael Waldmann
Maxim Friesen
Tobias Ferfers
Lukas Schreckenberg
Florian Hufen
J. Jasperneite
M. Wiering
OffRL
20
14
0
30 Mar 2021
1