[HTML][HTML] Reinforcement learning in urban network traffic signal control: A systematic literature review
M Noaeen, A Naik, L Goodman, J Crebo, T Abrar… - Expert Systems with …, 2022 - Elsevier
Improvement of traffic signal control (TSC) efficiency has been found to lead to improved
urban transportation and enhanced quality of life. Recently, the use of reinforcement …
urban transportation and enhanced quality of life. Recently, the use of reinforcement …
Collaborative autonomous driving—A survey of solution approaches and future challenges
Sooner than expected, roads will be populated with a plethora of connected and
autonomous vehicles serving diverse mobility needs. Rather than being stand-alone …
autonomous vehicles serving diverse mobility needs. Rather than being stand-alone …
IHG-MA: Inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control
Multi-agent deep reinforcement learning (MDRL) has been widely applied in multi-
intersection traffic signal control. The MDRL algorithms produce the decentralized …
intersection traffic signal control. The MDRL algorithms produce the decentralized …
Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
Hierarchical graph multi-agent reinforcement learning for traffic signal control
S Yang - Information Sciences, 2023 - Elsevier
Multi-agent reinforcement learning (MARL) is a promising algorithm for traffic signal control
(TSC), and graph neural networks make a further improvement on its learning capacity …
(TSC), and graph neural networks make a further improvement on its learning capacity …
Application of deep reinforcement learning in traffic signal control: An overview and impact of open traffic data
Persistent congestions which are varying in strength and duration in the dense traffic
networks are the most prominent obstacle towards sustainable mobility. Those types of …
networks are the most prominent obstacle towards sustainable mobility. Those types of …
A deep reinforcement learning approach to improve the learning performance in process control
Y Bao, Y Zhu, F Qian - Industrial & Engineering Chemistry …, 2021 - ACS Publications
Advanced model-based control methods have been widely used in industrial process
control, but excellent performance requires regular maintenance of its model. Reinforcement …
control, but excellent performance requires regular maintenance of its model. Reinforcement …
Cooperative traffic signal control with traffic flow prediction in multi-intersection
D Kim, O Jeong - Sensors, 2019 - mdpi.com
As traffic congestion in cities becomes serious, intelligent traffic signal control has been
actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning …
actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning …
An inductive heterogeneous graph attention-based multi-agent deep graph infomax algorithm for adaptive traffic signal control
S Yang, B Yang - Information fusion, 2022 - Elsevier
Adaptive traffic signal control (ATSC) facilitates alleviating traffic congestion. Multi-agent
deep reinforcement learning (MDRL) is a new promising algorithm for ATSC, and Graph …
deep reinforcement learning (MDRL) is a new promising algorithm for ATSC, and Graph …
Cooperative multi-agent actor–critic control of traffic network flow based on edge computing
Y Zhang, Y Zhou, H Lu, H Fujita - Future Generation Computer Systems, 2021 - Elsevier
Most of the existing traffic signal control strategies are hard to satisfy the real-time
requirements of traffic big data analysis, knowledge reasoning and decision making for …
requirements of traffic big data analysis, knowledge reasoning and decision making for …