Multi-agent reinforcement learning (MARL) methods have emerged as a promising solution
for multi-agent collaborative driving in the intersection and roundabout scenarios. However …

Reinforcement learning has been shown to have great potential applications in autonomous
driving. For collaborative driving scenarios, multi-agent reinforcement learning can be used …

Autonomous vehicles trained through Multi-Agent Reinforcement Learning (MARL) have
shown impressive results in many driving scenarios. However, the performance of these …

Reinforcement Learning (RL) is a potent tool for sequential decision-making and has
achieved performance surpassing human capabilities across many challenging real-world …

The development of autonomous vehicles has shown great potential to enhance the
efficiency and safety of transportation systems. However, the decision-making issue in …

Connected and autonomous vehicles offer the possibility to carry out control strategies, thus
having great potential to improve traffic efficiency and road safety. The efficient passage of …

Abstract The application of Deep Reinforcement Learning (DRL) has significantly impacted
the development of autonomous driving technology in the field of intelligent transportation …

With the advent of machine learning, several autonomous driving tasks have become easier
to accomplish. Nonetheless, the proliferation of autonomous vehicles in urban traffic …

As increasing numbers of autonomous vehicles (AVs) are being deployed, it is important to
construct a multi-agent self-driving (MASD) system for navigating traffic flows of AVs. In an …

Autonomous driving is an emerging technology that has developed rapidly over the last
decade. Benefiting from information sharing and collaborative decision-making of connected …