To improve efficiency and reduce failures in autonomous vehicles, research has focused on
developing robust and safe learning methods that take into account disturbances in the …

This paper utilizes parallel control to investigate the problem of event-triggered deep
reinforcement learning and develops an event-triggered deep Q-network (ETDQN) for …

Human intervention is an effective way to inject human knowledge into the training loop of
reinforcement learning, which can bring fast learning and ensured training safety. Given the …

It is still a challenge to realize safe and fast autonomous driving through deep reinforcement
learning (DRL). Most autonomous driving reinforcement learning models are subject to a …

Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …

As an indispensable branch of machine learning (ML), reinforcement learning (RL) plays a
prominent role in the decision-making process of autonomous driving (AD), which enables …

Most methods that attempt to tackle the problem of Autonomous Driving and overtaking
usually try to either directly minimize an objective function or iteratively in a Reinforcement …

Despite promising progress in reinforcement learning (RL), developing algorithms for
autonomous driving (AD) remains challenging: one of the critical issues being the absence …

This paper investigates the vision-based autonomous driving with deep learning and
reinforcement learning methods. Different from the end-to-end learning method, our method …

The majority of current studies on autonomous vehicle control via deep reinforcement
learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which …