Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications
Reinforcement learning (RL) algorithms have been around for decades and employed to
solve various sequential decision-making problems. These algorithms, however, have faced …
solve various sequential decision-making problems. These algorithms, however, have faced …
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 …
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 …
[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 …
Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems
A Boukerche, Y Tao, P Sun - Computer networks, 2020 - Elsevier
In recent years, the Intelligent transportations system (ITS) has received considerable
attention, due to higher demands for road safety and efficiency in highly interconnected road …
attention, due to higher demands for road safety and efficiency in highly interconnected road …
[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 …
Reinforcement learning based energy management systems and hydrogen refuelling stations for fuel cell electric vehicles: An overview
R Venkatasatish, C Dhanamjayulu - International Journal of Hydrogen …, 2022 - Elsevier
This paper examines the current state of the art of hydrogen refuelling stations-based
production and storage systems for fuel cell hybrid electric vehicles (FCHEV). Nowadays …
production and storage systems for fuel cell hybrid electric vehicles (FCHEV). Nowadays …
Adaptive Traffic Signal Control for large-scale scenario with Cooperative Group-based Multi-agent reinforcement learning
T Wang, J Cao, A Hussain - Transportation research part C: emerging …, 2021 - Elsevier
Recent research reveals that reinforcement learning can potentially perform optimal
decision-making compared to traditional methods like Adaptive Traffic Signal Control …
decision-making compared to traditional methods like Adaptive Traffic Signal Control …
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 …
Large-scale traffic signal control using a novel multiagent reinforcement learning
Finding the optimal signal timing strategy is a difficult task for the problem of large-scale
traffic signal control (TSC). Multiagent reinforcement learning (MARL) is a promising method …
traffic signal control (TSC). Multiagent reinforcement learning (MARL) is a promising method …