An intelligent IoT based traffic light management system: deep reinforcement learning
Traffic is one of the indispensable problems of modern societies, which leads to undesirable
consequences such as time wasting and greater possibility of accidents. Adaptive Traffic …
consequences such as time wasting and greater possibility of accidents. Adaptive Traffic …
Context-aware multiagent broad reinforcement learning for mixed pedestrian-vehicle adaptive traffic light control
Efficient traffic light control is a critical part of realizing smart transportation. In particular,
deep reinforcement learning (DRL) algorithms that use deep neural networks (DNNs) have …
deep reinforcement learning (DRL) algorithms that use deep neural networks (DNNs) have …
A large-scale traffic signal control algorithm based on multi-layer graph deep reinforcement learning
Due to its capability in handling complex urban intersection environments, deep
reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control …
reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control …
An overview: Attention mechanisms in multi-agent reinforcement learning
K Hu, K Xu, Q Xia, M Li, Z Song, L Song, N Sun - Neurocomputing, 2024 - Elsevier
In recent years, in the field of Multi-Agent Systems (MAS), significant progress has been
made in the research of algorithms that combine Reinforcement Learning (RL) with Attention …
made in the research of algorithms that combine Reinforcement Learning (RL) with Attention …
Extensible prototype learning for real‐time traffic signal control
Y Han, H Lee, Y Kim - Computer‐Aided Civil and Infrastructure …, 2023 - Wiley Online Library
Congestion resolution continues to remain a challenge even though various signal control
systems have been developed for traffic‐intersection control. To address this issue …
systems have been developed for traffic‐intersection control. To address this issue …
AGRCNet: communicate by attentional graph relations in multi-agent reinforcement learning for traffic signal control
T Ma, K Peng, H Rong, Y Qian - Neural Computing and Applications, 2023 - Springer
Traffic signal control (TSC) can be described as a multi-agent cooperative game. To realize
cooperation, multi-agent reinforcement learning (MARL) is a significant approach, with …
cooperation, multi-agent reinforcement learning (MARL) is a significant approach, with …
Adaptive Multi-Agent Deep Mixed Reinforcement Learning for Traffic Light Control
Despite significant advancements in Multi-Agent Deep Reinforcement Learning (MADRL)
approaches for Traffic Light Control (TLC), effectively coordinating agents in diverse traffic …
approaches for Traffic Light Control (TLC), effectively coordinating agents in diverse traffic …
Data-Driven Distributed Adaptive Consensus Tracking of Nonlinear Multiagent Systems: A Controller-Based Dynamic Linearization Method
For the consensus tracking of multiagent systems (MASs), most of existing distributed control
methods need to design the controller structure with availability of physical model or …
methods need to design the controller structure with availability of physical model or …
BRGR: Multi-agent cooperative reinforcement learning with bidirectional real-time gain representation
X He, H Ge, L Sun, Q Li, Y Hou - Applied Intelligence, 2023 - Springer
In the multi-agent cooperative decision-making process, an agent needs to learn
cooperatively with its neighbors to obtain the optimal strategy. The actions of agents can be …
cooperatively with its neighbors to obtain the optimal strategy. The actions of agents can be …
HD‐RMPC: A Hierarchical Distributed and Robust Model Predictive Control Framework for Urban Traffic Signal Timing
Due to the nonlinearity and dynamics of transportation systems, traffic signal control (TSC) in
urban traffic networks has always been an important challenge. In recent years, model …
urban traffic networks has always been an important challenge. In recent years, model …