Combining multi-agent deep deterministic policy gradient and rerouting technique to improve traffic network performance under mixed traffic conditions
In the future, mixed traffic flow will include two types of vehicles: connected autonomous
vehicles (CAVs) and human-driven vehicles (HDVs). CAVs emerge as new solutions to …
vehicles (CAVs) and human-driven vehicles (HDVs). CAVs emerge as new solutions to …
A deep reinforcement learning approach for traffic signal control optimization
Z Li, C Xu, G Zhang - arXiv preprint arXiv:2107.06115, 2021 - arxiv.org
Inefficient traffic signal control methods may cause numerous problems, such as traffic
congestion and waste of energy. Reinforcement learning (RL) is a trending data-driven …
congestion and waste of energy. Reinforcement learning (RL) is a trending data-driven …
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 …
A Novel Multi-Agent Deep RL Approach for Traffic Signal Control
As travel demand increases and urban traffic condition becomes more complicated,
applying multi-agent deep reinforcement learning (MARL) to traffic signal control becomes …
applying multi-agent deep reinforcement learning (MARL) to traffic signal control becomes …
CMARL: A Multi-agent Deep Reinforcement Learning Model with Emphasis on Communication Content
A Chang, Y Ji, C Wang, Y Bie - 2024 - preprints.org
In recent years, the viability of employing multi-agent reinforcement learning technology for
adaptive traffic signal control has been extensively validated. However, owing to restricted …
adaptive traffic signal control has been extensively validated. However, owing to restricted …
Multi-agent deep reinforcement learning for decentralized cooperative traffic signal control
The traffic congestion becomes a severe problem in almost every city, and intelligent
transportation systems make it possible for an adaptive traffic signal control system to …
transportation systems make it possible for an adaptive traffic signal control system to …
Collaborative Traffic Signal Automation Using Deep Q-Learning
Multi-agent deep reinforcement learning (MDRL) is a popular choice for multi-intersection
traffic signal control, generating decentralized cooperative traffic signal strategies in specific …
traffic signal control, generating decentralized cooperative traffic signal strategies in specific …
Decentralized deep reinforcement learning for network level traffic signal control
J Guo - 2020 - search.proquest.com
Taming traffic congestion and its negative social-environmental impacts has long been a
daunting problem for traffic engineers and transportation planners. Besides adding road …
daunting problem for traffic engineers and transportation planners. Besides adding road …
Optimizing Traffic Signal Control in Mixed Traffic Scenarios: A Predictive Traffic Information-Based Deep Reinforcement Learning Approach
Z Zhang, B Zhou, B Zhang, P Cheng… - 2024 Forum for …, 2024 - ieeexplore.ieee.org
The rapid advancement of Connected Autonomous Vehicles (CAVs) is a driving force in the
evolution of smart cities and Intelligent Transportation Systems (ITS). This has spurred …
evolution of smart cities and Intelligent Transportation Systems (ITS). This has spurred …
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 …