Multi-agent reinforcement learning for traffic signal control through universal communication method
How to coordinate the communication among intersections effectively in real complex traffic
scenarios with multi-intersection is challenging. Existing approaches only enable the …
scenarios with multi-intersection is challenging. Existing approaches only enable the …
[PDF][PDF] GPLight: Grouped Multi-agent Reinforcement Learning for Large-scale Traffic Signal Control.
The use of multi-agent reinforcement learning (MARL) methods in coordinating traffic lights
(CTL) has become increasingly popular, treating each intersection as an agent. However …
(CTL) has become increasingly popular, treating each intersection as an agent. However …
Fedlight: Federated reinforcement learning for autonomous multi-intersection traffic signal control
Although Reinforcement Learning (RL) has been successfully applied in traffic control, it
suffers from the problems of high average vehicle travel time and slow convergence to …
suffers from the problems of high average vehicle travel time and slow convergence to …
Learning scalable multi-agent coordination by spatial differentiation for traffic signal control
J Liu, H Zhang, Z Fu, Y Wang - Engineering Applications of Artificial …, 2021 - Elsevier
The intelligent control of the traffic signal is critical to the optimization of transportation
systems. To achieve global optimal traffic efficiency in large-scale road networks, recent …
systems. To achieve global optimal traffic efficiency in large-scale road networks, recent …
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 …
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 …
Distributed signal control of arterial corridors using multi-agent deep reinforcement learning
Traffic congestion at signalized intersections often leads to serious impacts on adjacent
intersections on a corridor. To enhance intersections' throughput efficiency, traffic signals are …
intersections on a corridor. To enhance intersections' throughput efficiency, traffic signals are …
Dualight: Enhancing traffic signal control by leveraging scenario-specific and scenario-shared knowledge
Reinforcement learning has been revolutionizing the traditional traffic signal control task,
showing promising power to relieve congestion and improve efficiency. However, the …
showing promising power to relieve congestion and improve efficiency. However, the …
Cooperative Traffic Signal Control Using a Distributed Agent-Based Deep Reinforcement Learning With Incentive Communication
Deep Reinforcement Learning has shown some promise in dynamic traffic signal control by
adapting to real-time traffic conditions. However, multi-intersection control presents …
adapting to real-time traffic conditions. However, multi-intersection control presents …
Colight: Learning network-level cooperation for traffic signal control
Cooperation among the traffic signals enables vehicles to move through intersections more
quickly. Conventional transportation approaches implement cooperation by pre-calculating …
quickly. Conventional transportation approaches implement cooperation by pre-calculating …