Multi-agent reinforcement learning based on two-step neighborhood experience for traffic light control
YC Luo, CW Tsai - Proceedings of the 2021 ACM International …, 2021 - dl.acm.org
Several recent studies pointed out that an effective traffic signal/light control strategy will be
able to mitigate the traffic congestion problem, and therefore variants of solutions have been …
able to mitigate the traffic congestion problem, and therefore variants of solutions have been …
A graph attention mechanism based multi-agent reinforcement learning method for efficient traffic light control
C Su, Y Yan, T Wang, B Zhang… - … and Mobile Computing …, 2021 - ieeexplore.ieee.org
Traffic light control is vital for the efficiency of urban transportation. Recently, the increasing
of vehicles has brought great challenges to the traffic light control system. However …
of vehicles has brought great challenges to the traffic light control system. However …
Multi-agent deep reinforcement learning with actor-attention-critic for traffic light control
B Wang, ZK He, JF Sheng… - Proceedings of the …, 2023 - journals.sagepub.com
In recent years, with the increase of urbanization and car ownership, urban traffic congestion
have become increasingly prominent. Traffic light control can effectively reduce urban traffic …
have become increasingly prominent. Traffic light control can effectively reduce urban traffic …
Multi-agent broad reinforcement learning for intelligent traffic light control
Intelligent traffic light control (ITLC) aims to relieve traffic congestion. Some multi-agent deep
reinforcement learning (MADRL) algorithms have been proposed for ITLC, and most of them …
reinforcement learning (MADRL) algorithms have been proposed for ITLC, and most of them …
Auto-learning communication reinforcement learning for multi-intersection traffic light control
Multi-agent reinforcement learning is a promising solution to achieve intelligent traffic light
control by regarding each intersection as an independent agent. However, agents encounter …
control by regarding each intersection as an independent agent. However, agents encounter …
Mixlight: Mixed-agent cooperative reinforcement learning for traffic light control
Optimizing traffic light configuration is viewed as a method to increase the traffic throughput
in urban cities. Recent studies have employed reinforcement learning to optimize the traffic …
in urban cities. Recent studies have employed reinforcement learning to optimize the traffic …
Multi-agent deep reinforcement learning for urban traffic light control in vehicular networks
As urban traffic condition is diverse and complicated, applying reinforcement learning to
reduce traffic congestion becomes one of the hot and promising topics. Especially, how to …
reduce traffic congestion becomes one of the hot and promising topics. Especially, how to …
STMARL: A spatio-temporal multi-agent reinforcement learning approach for cooperative traffic light control
The development of intelligent traffic light control systems is essential for smart
transportation management. While some efforts have been made to optimize the use of …
transportation management. While some efforts have been made to optimize the use of …
A traffic light control method based on multi-agent deep reinforcement learning algorithm
D Liu, L Li - Scientific Reports, 2023 - nature.com
Intelligent traffic light control (ITLC) algorithms are very efficient for relieving traffic
congestion. Recently, many decentralized multi-agent traffic light control algorithms are …
congestion. Recently, many decentralized multi-agent traffic light control algorithms are …
Joint Control of Lane Allocation and Traffic Light for Changeable-Lane Intersection Based on Reinforcement Learning
ESA Gyarteng, R Shi, Y Long - Proceedings of the 2021 4th International …, 2021 - dl.acm.org
Deep reinforcement learning based intelligent traffic light is an increasing tendency for
improving the traffic condition due to its promising control performance. The current works …
improving the traffic condition due to its promising control performance. The current works …