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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

PeerJ Computer Science (PeerJ Comput. Sci.), 2020
9 April 2020
L. N. Alegre
A. Bazzan
Bruno C. da Silva
ArXiv (abs)PDFHTML

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

6 / 6 papers shown
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
455
1
0
17 Mar 2025
The Max-Min Formulation of Multi-Objective Reinforcement Learning: From
  Theory to a Model-Free Algorithm
The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
Giseung Park
Woohyeon Byeon
Seongmin Kim
Elad Havakuk
Amir Leshem
Youngchul Sung
304
8
0
12 Jun 2024
Improving Intrinsic Exploration by Creating Stationary Objectives
Improving Intrinsic Exploration by Creating Stationary ObjectivesInternational Conference on Learning Representations (ICLR), 2023
Roger Creus Castanyer
Javier Civera
Taihú Pire
OffRL
496
4
0
27 Oct 2023
Deep Reinforcement Learning-based Intelligent Traffic Signal Controls
  with Optimized CO2 emissions
Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions
Pedram Agand
Alexey Iskrov
Mo Chen
308
6
0
19 Oct 2023
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
332
13
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 ControlInternational Conference on Machine Learning and Applications (ICMLA), 2021
Arthur Muller
Vishal S. Rangras
Georg Schnittker
Michael Waldmann
Maxim Friesen
Tobias Ferfers
Lukas Schreckenberg
Florian Hufen
J. Jasperneite
M. Wiering
OffRL
194
19
0
30 Mar 2021
1
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