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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
#reinforcement learning Sep 2026

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. · 4 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-world urban deployment.

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

Coordinated traffic signal control across urban networks must adapt to changing demand while satisfying operational constraints. Multi-objective distributed model predictive control (DMPC) can construct feasible signal plans online, but prescribed rules for selecting among trade-off solutions cannot learn from realized closed-loop outcomes. We propose SelectLight, which implements post-optimization selection by allowing a multi-agent reinforcement learning (MARL) policy to choose directly from plans generated online by DMPC. At each control update, state-pruned multi-objective dynamic programming (SP-MODP) evaluates plans with a Newellian point--spatial queue model and returns a bounded set of mutually nondominated candidate signal plans for total queueing delay, peak queue accumulation, and total number of stops. A topology-aware attention policy trained with independent proximal policy optimization (IPPO) selects one unmodified plan from each variable-size set. This confines learning to candidate selection, preserves the prescribed signal timing constraints, and leaves the selected plan and its predicted objective trade-offs available for inspection. Experiments on two 28-intersection SUMO networks show that SelectLight achieves the best delay-related performance and that its advantage widens with demand. At twice the baseline demand, it reduces queueing delay and waiting time by 5.57% and 6.44%, respectively, relative to the strongest baseline. SelectLight also incurs the lowest transfer loss under every tested demand shift. With a 120 s prediction horizon, the per-intersection 99th-percentile SP-MODP solution time is 5.408 ms, well below the 5 s control interval.

Lyuzhou Luo, Chaopeng Tan, Zhengyong Gao et al. · 0 citations

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