… single-agent reinforcement learning usually … a multi-agent reinforcement learning (MARL)
approach to train and coordinate the policies of all vehicles to handle unsignalized intersection …

… unsignalized intersection environment, where the CAVs and HDVs coexist, was modeled as
a model-free multi-agent system to solve the cooperative decision-making … multi-agent task, …

… Artificial intelligence constitutes a framework with tools as leverage for constructing
intelligent, autonomous control and decision-making algorithms in an attempt to provide a more …

… multi-directional intersection scenarios. Antonio et al. [21] propose an automatic intersection
… dynamics of traffic and multi-agent deep reinforcement learning to control the speed of …

… decision control framework for unsignalized intersections based on Soft … decision-making
model based on DRL is constructed. Within the framework of the DRL-based decision-making …

… In [26], authors propose a multiagent approach in which road … is trained to handle unsignalized
intersections using Deep Q-… change decision making using deep reinforcement learning,” …

… traffic environment mainly include machine learning, fuzzy logic, … Each intersection maintains
an independent decision-making … of two red-signalized intersections and used Legendre …

… This study designs a multi-level objectives framework for AVs’ trajectory decision-making
based on multi-agent DRL. The proposed method can realize the flexible multi-level objective …

… , particularly at signalized intersections. This problem not only … The fixed cycle of traffic lights
at these intersections is one of … in machine learning (ML) is sequential decision-making. The …

… This study explores the application of multi-agent reinforcement learning (MARL) to
enhance the decision-making, safety, and passenger comfort of Autonomous Vehicles (AVs) at …