Cooperative traffic signal control using multi-step return and off-policy asynchronous advantage actor-critic graph algorithm
Intelligent traffic signal control helps to reduce traffic congestion and thus has been studied
for a few decades. Multi-intersection cooperative traffic signal control (CTSC), which is more …
for a few decades. Multi-intersection cooperative traffic signal control (CTSC), which is more …
Traffic signal control with reinforcement learning based on region-aware cooperative strategy
With the increase of private cars, traditional traffic signal control methods cannot alleviate the
traffic congestion problem. Reinforcement learning (RL) is increasingly used in adaptive …
traffic congestion problem. Reinforcement learning (RL) is increasingly used in adaptive …
GraphLight: graph-based reinforcement learning for traffic signal control
Z Zeng - 2021 ieee 6th international conference on computer …, 2021 - ieeexplore.ieee.org
Adaptive traffic signal control can alleviate traffic congestion and improve throughput. A
decentralized multi-agent reinforcement learning (MARL) is a promising data-driven method …
decentralized multi-agent reinforcement learning (MARL) is a promising data-driven method …
Cooperative multi-agent actor–critic control of traffic network flow based on edge computing
Y Zhang, Y Zhou, H Lu, H Fujita - Future Generation Computer Systems, 2021 - Elsevier
Most of the existing traffic signal control strategies are hard to satisfy the real-time
requirements of traffic big data analysis, knowledge reasoning and decision making for …
requirements of traffic big data analysis, knowledge reasoning and decision making for …
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 …
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning
Inefficient traffic control may cause numerous problems such as traffic congestion and
energy waste. This paper proposes a novel multi-agent reinforcement learning method …
energy waste. This paper proposes a novel multi-agent reinforcement learning method …
Multi-agent deep deterministic policy gradient for traffic signal control on urban road network
S Li - 2020 IEEE International Conference on Advances in …, 2020 - ieeexplore.ieee.org
Aiming at how to effectively use information in urban traffic signal control to optimize traffic
conditions and ensure the adaptability and robustness of the control algorithm, this paper …
conditions and ensure the adaptability and robustness of the control algorithm, this paper …
IHG-MA: Inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control
Multi-agent deep reinforcement learning (MDRL) has been widely applied in multi-
intersection traffic signal control. The MDRL algorithms produce the decentralized …
intersection traffic signal control. The MDRL algorithms produce the decentralized …
CVLight: Decentralized learning for adaptive traffic signal control with connected vehicles
This paper develops a decentralized reinforcement learning (RL) scheme for multi-
intersection adaptive traffic signal control (TSC), called “CVLight”, that leverages data …
intersection adaptive traffic signal control (TSC), called “CVLight”, that leverages data …
Distributed agent-based deep reinforcement learning for large scale traffic signal control
Traffic signal control (TSC) is an established yet challenging engineering solution that
alleviates traffic congestion by coordinating vehicles' movements at road intersections …
alleviates traffic congestion by coordinating vehicles' movements at road intersections …