Graph-based deep learning for communication networks: A survey
W Jiang - Computer Communications, 2022 - Elsevier
Communication networks are important infrastructures in contemporary society. There are
still many challenges that are not fully solved and new solutions are proposed continuously …
still many challenges that are not fully solved and new solutions are proposed continuously …
An overview on the application of graph neural networks in wireless networks
In recent years, with the rapid enhancement of computing power, deep learning methods
have been widely applied in wireless networks and achieved impressive performance. To …
have been widely applied in wireless networks and achieved impressive performance. To …
Deep multi-agent reinforcement learning for highway on-ramp merging in mixed traffic
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …
Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic
Autonomous driving has attracted significant research interests in the past two decades as it
offers many potential benefits, including releasing drivers from exhausting driving and …
offers many potential benefits, including releasing drivers from exhausting driving and …
Space-weighted information fusion using deep reinforcement learning: The context of tactical control of lane-changing autonomous vehicles and connectivity range …
The connectivity aspect of connected autonomous vehicles (CAV) is beneficial because it
facilitates dissemination of traffic-related information to vehicles through Vehicle-to-External …
facilitates dissemination of traffic-related information to vehicles through Vehicle-to-External …
Multi-agent reinforcement learning for autonomous vehicles: A survey
In the near future, autonomous vehicles (AVs) may cohabit with human drivers in mixed
traffic. This cohabitation raises serious challenges, both in terms of traffic flow and individual …
traffic. This cohabitation raises serious challenges, both in terms of traffic flow and individual …
Hybrid car following control for CAVs: Integrating linear feedback and deep reinforcement learning to stabilize mixed traffic
This paper introduces a novel hybrid car-following strategy for connected automated
vehicles (CAVs) to mitigate traffic oscillations while simultaneously improving CAV car …
vehicles (CAVs) to mitigate traffic oscillations while simultaneously improving CAV car …
Leveraging the capabilities of connected and autonomous vehicles and multi-agent reinforcement learning to mitigate highway bottleneck congestion
Active Traffic Management strategies are often adopted in real-time to address such sudden
flow breakdowns. When queuing is imminent, Speed Harmonization (SH), which adjusts …
flow breakdowns. When queuing is imminent, Speed Harmonization (SH), which adjusts …
Leveraging vehicle connectivity and autonomy for highway bottleneck congestion mitigation using reinforcement learning
Automation and connectivity based platforms have great potential for managing highway
traffic congestion including bottlenecks. Speed harmonisation (SH), one of such platforms, is …
traffic congestion including bottlenecks. Speed harmonisation (SH), one of such platforms, is …
Multi-agent decision-making modes in uncertain interactive traffic scenarios via graph convolution-based deep reinforcement learning
X Gao, X Li, Q Liu, Z Li, F Yang, T Luan - Sensors, 2022 - mdpi.com
As one of the main elements of reinforcement learning, the design of the reward function is
often not given enough attention when reinforcement learning is used in concrete …
often not given enough attention when reinforcement learning is used in concrete …