A novel cooperative MARL-based approach for adaptive traffic signal control in multi-intersection networks that significantly outperforms existing methods in relation to average pheromone intensity, average noise emission, and average waiting time is proposed.
T. Haddad· Transportation Research Reco...· 0 citations
The findings demonstrate the potential of DRL-based traffic signal control in controlled simulation conditions and highlight that algorithm performance is strongly influenced by traffic policy design and environmental complexity.
D. Prastiyanto, A. A. Manaf, Muhammad Ahnaf Maulana et al.· Scientific Reports· 0 citations
A distributed TSC model based on the Soft Actor-Critic (SAC) reinforcement learning algorithm that demonstrates the model’s effectiveness, adaptability, and potential for deployment in intelligent traffic management systems is proposed.
Yunxue Lu, Chang-Ze Li, Hao Yu et al.· Journal of Intelligent Trans...· 5 citations
HiLLTS, an LLM-guided traffic signal control framework that employs a hierarchical three-layer architecture consisting of a central coordination agent, a district layer and multiple cluster-level intersection agents, is proposed.
An algorithm-agnostic Distributionally Robust MARL framework integrating an adaptive Contextual-Bandit Worst-Case Estimator (CB-WCE) co-evolves with the traffic controllers by dynamically generating adversarial demand mixtures during training, demonstrating the framework's scalability and potential for resilient real-w...
Shuwei Pei, Joran Borger, Arda Kosay et al.· 0 citations
SelectLight is proposed, which implements post-optimization selection by allowing a multi-agent reinforcement learning (MARL) policy to choose directly from plans generated online by DMPC, and achieves the best delay-related performance and that its advantage widens with demand.
Lyuzhou Luo, Chaopeng Tan, Zheng-Yong Gao et al.· 0 citations
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