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 …
[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 …
[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 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 …
Applications of deep learning in intelligent transportation systems
AK Haghighat, V Ravichandra-Mouli… - Journal of Big Data …, 2020 - Springer
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 …
[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 …
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 …
Traffic signal optimization for partially observable traffic system and low penetration rate of connected vehicles
Observability and controllability are two critical requirements for a partially observable
transportation system. This paper proposes a stepwise signal optimization framework with …
transportation system. This paper proposes a stepwise signal optimization framework with …
Deep reinforcement learning versus evolution strategies: A comparative survey
AY Majid, S Saaybi, V Francois-Lavet… - … on Neural Networks …, 2023 - ieeexplore.ieee.org
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 …