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

9 April 2020

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

6 / 6 papers shown

Title
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration Amir Baghi Jens Sjölund Joakim Bergdahl Linus Gisslén Alessandro Sestini 134 0 0 17 Mar 2025
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 51 3 0 12 Jun 2024
Improving Intrinsic Exploration by Creating Stationary Objectives Roger Creus Castanyer Javier Civera Taihú Pire OffRL 115 4 0 27 Oct 2023
Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions Pedram Agand Alexey Iskrov Mo Chen 60 5 0 19 Oct 2023
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 100 9 0 23 Jun 2022
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 59 15 0 30 Mar 2021