H. T. Trinh

Utilizing proximal policy optimization-based multi-agent reinforcement learning to enhance traffic performance and eco-diving in urban areas

Connected and automated vehicles are expected to play a key role in alleviating urban traffic congestion in transportation systems. However, coordinating traffic lights and connected and automated vehicles remains a challenging problem in mixed-autonomy environments. This paper proposes a modified proximal policy optim...