[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 …
[HTML][HTML] Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation
In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …
interest in reinforcement learning (RL) within the traffic and transportation community …
A review of cooperative multi-agent deep reinforcement learning
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] 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 …
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 …
A review of cooperative multi-agent deep reinforcement learning
Deep Reinforcement Learning has made significant progress in multi-agent systems in
recent years. In this review article, we have focused on presenting recent approaches on …
recent years. In this review article, we have focused on presenting recent approaches on …
Deep reinforcement learning in transportation research: A review
Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
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 …
Applications of deep learning in intelligent transportation systems
Abstract In recent years, Intelligent Transportation Systems (ITS) have seen efficient and
faster development by implementing deep learning techniques in problem domains which …
faster development by implementing deep learning techniques in problem domains which …
Deep reinforcement learning versus evolution strategies: A comparative survey
Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-
level control in many sequential decision-making problems, yet many open challenges still …
level control in many sequential decision-making problems, yet many open challenges still …