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

Deep reinforcement learning-based adaptive traffic signal control in urban networks

Results demonstrate the usefulness of deep reinforcement learning in the creation of intelligent and adaptive traffic signal control services in the city and prove the usefulness of computational feasibility and robustness even when the size of the network grows.

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.

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.

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

Area traffic signal coordination control method based on action value weighted QMIX

An action value weighted QMIX (AVW-QMIX) based cooperative traffic signal control method, which models a multi-intersection traffic network in a region as a multi-agent reinforcement learning system, and uses each local signal agent to sense the traffic flow information of its incoming lane.

Adaptive Dynamic Model-Free Neuro-Fuzzy System for Traffic Signal Control

A traffic signal controller that learns online without requiring a traffic model and can reduce the blindness of learning by using experience and the inaccuracy of the traffic model is designed.