Optimized adaptive traffic management using double DQN and prioritized experience replay in a multi-agent traffic control setup

Dynamic traffic signal scheduling system based on adaptive quad agent Double Deep Q -network algorithm

Simulation results indicate that the proposed Adaptive Quad-Agent Double Deep Q-Network model effectively supports dynamic signal phase adaptation, minimizes congestion, and provides more accurate queue length estimations under complex traffic conditions.

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.

A Decentralized Traffic Signal Control Framework Using Multiagent Reinforcement Learning With Real-World Operational Constraints

Reinforcement learning for traffic signal control in large-scale transportation networks: a systematic literature review

A systematic and up-to-date review of RL-based methods for large-scale TSC in traffic simulation environments, transportation modalities, and advances in methodologies is presented.

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.

Optimization of last-mile delivery using dynamic path algorithms based on reinforcement learning

The study uses a dynamic routing framework to create adaptive routing strategies, centered on a customized deep Q-network, that works well in variable traffic scenarios and effectively adapts to peak and off-peak delivery windows.