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Aug 2026

Multi-Intersection Traffic Signal Control Based on Multi-agent Reinforcement Learning: A Cooperative Approach

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 · 0 citations
Open access Aug 2026

Deep reinforcement learning-based traffic signal control in multi-intersection environments: a comparative study of DQN variants

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. · 0 citations

Soft Actor-Critic based regional traffic signal control in connected environment and its application in priority signal control

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. · 5 citations
Preprint Jul 2026

A Distributionally Robust Multi-agent Reinforcement Learning Framework for Intelligent Intersection Control

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
Preprint Aug 2026

SelectLight: Learning to Select Signal Plans Generated by Distributed Model Predictive Control for Urban Traffic Networks

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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