A review of cooperative multi-agent deep reinforcement learning
A Oroojlooy, D Hajinezhad - Applied Intelligence, 2023 - Springer
Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …
systems in recent years. The aim of this review article is to provide an overview of recent …
[HTML][HTML] Reinforcement learning in urban network traffic signal control: A systematic literature review
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 …
Deep reinforcement learning for intelligent transportation systems: A survey
Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …
approaches bring out a new research direction for all control-based systems, eg, in …
Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control
Traffic congestion plagues cities around the world. Recent years have witnessed an
unprecedented trend in applying reinforcement learning for traffic signal control. However …
unprecedented trend in applying reinforcement learning for traffic signal control. However …
Presslight: Learning max pressure control to coordinate traffic signals in arterial network
Traffic signal control is essential for transportation efficiency in road networks. It has been a
challenging problem because of the complexity in traffic dynamics. Conventional …
challenging problem because of the complexity in traffic dynamics. Conventional …
Learning phase competition for traffic signal control
Increasingly available city data and advanced learning techniques have empowered people
to improve the efficiency of our city functions. Among them, improving urban transportation …
to improve the efficiency of our city functions. Among them, improving urban transportation …
Metalight: Value-based meta-reinforcement learning for traffic signal control
Using reinforcement learning for traffic signal control has attracted increasing interests
recently. Various value-based reinforcement learning methods have been proposed to deal …
recently. Various value-based reinforcement learning methods have been proposed to deal …
A review on swarm intelligence and evolutionary algorithms for solving the traffic signal control problem
The rapid development of urban cities coupled with the rise in population has led to an
exponentially growing number of vehicles on the roads for the latter to commute. This is …
exponentially growing number of vehicles on the roads for the latter to commute. This is …
Deep reinforcement learning for traffic signal control: A review
Traffic congestion is a complex, vexing, and growing issue day by day in most urban areas
worldwide. The integration of the newly emerging deep learning approach and the …
worldwide. The integration of the newly emerging deep learning approach and the …
[HTML][HTML] Deep reinforcement learning for traffic signal control with consistent state and reward design approach
Abstract Intelligent Transportation Systems are essential due to the increased number of
traffic congestion problems and challenges nowadays. Traffic Signal Control (TSC) plays a …
traffic congestion problems and challenges nowadays. Traffic Signal Control (TSC) plays a …