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 …
Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation
Traffic signal control is an important and challenging real-world problem that has recently
received a large amount of interest from both transportation and computer science …
received a large amount of interest from both transportation and computer science …
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 …
Smarts: An open-source scalable multi-agent rl training school for autonomous driving
Interaction is fundamental in autonomous driving (AD). Despite more than a decade of
intensive R&D in AD, how to dynamically interact with diverse road users in various contexts …
intensive R&D in AD, how to dynamically interact with diverse road users in various contexts …
[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 …
Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario
Traffic signal control is an emerging application scenario for reinforcement learning. Besides
being as an important problem that affects people's daily life in commuting, traffic signal …
being as an important problem that affects people's daily life in commuting, traffic signal …
A survey on traffic signal control methods
Traffic signal control is an important and challenging real-world problem, which aims to
minimize the travel time of vehicles by coordinating their movements at the road …
minimize the travel time of vehicles by coordinating their movements at the road …
Graph neural networks for intelligent transportation systems: A survey
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
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 …
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning
Inefficient traffic control may cause numerous problems such as traffic congestion and
energy waste. This paper proposes a novel multi-agent reinforcement learning method …
energy waste. This paper proposes a novel multi-agent reinforcement learning method …