[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 …
A Cooperative DRL Approach for Autonomous Traffic Prioritization in Mixed Vehicles Scenarios
The number of connected and automated vehicles in urban areas will gradually increase in
the near future. As a consequence, mixed traffic made of both regular human-driven and …
the near future. As a consequence, mixed traffic made of both regular human-driven and …
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 …
Courteous behavior of automated vehicles at unsignalized intersections via reinforcement learning
S Yan, T Welschehold, D Büscher… - IEEE Robotics and …, 2021 - ieeexplore.ieee.org
The transition from today's mostly human-driven traffic to a purely automated one will be a
gradual evolution, with the effect that we will likely experience mixed traffic in the near future …
gradual evolution, with the effect that we will likely experience mixed traffic in the near future …
Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery
In urban environments, the complex and uncertain intersection scenarios are challenging for
autonomous driving. To ensure safety, it is crucial to develop an adaptive decision making …
autonomous driving. To ensure safety, it is crucial to develop an adaptive decision making …
Behaviorally diverse traffic simulation via reinforcement learning
S Shiroshita, S Maruyama, D Nishiyama… - 2020 IEEE/RSJ …, 2020 - ieeexplore.ieee.org
Traffic simulators are important tools in autonomous driving development. While continuous
progress has been made to provide developers more options for modeling various traffic …
progress has been made to provide developers more options for modeling various traffic …
Graph convolution-based deep reinforcement learning for multi-agent decision-making in interactive traffic scenarios
A reliable multi-agent decision-making system is highly demanded for safe and efficient
operations of connected and autonomous vehicles (CAVs). In order to represent the mutual …
operations of connected and autonomous vehicles (CAVs). In order to represent the mutual …
iplan: Intent-aware planning in heterogeneous traffic via distributed multi-agent reinforcement learning
Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging
for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of …
for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of …
A Reinforcement Learning Benchmark for Autonomous Driving in General Urban Scenarios
Reinforcement learning (RL) has gained significant interest for its potential to improve
decision and control in autonomous driving. However, current approaches have yet to …
decision and control in autonomous driving. However, current approaches have yet to …
HARL: A novel hierachical adversary reinforcement learning for automoumous intersection management
G Li, J Wu, Y He - arXiv preprint arXiv:2205.02428, 2022 - arxiv.org
As an emerging technology, Connected Autonomous Vehicles (CAVs) are believed to have
the ability to move through intersections in a faster and safer manner, through effective …
the ability to move through intersections in a faster and safer manner, through effective …