Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to
enable agents to learn and perform tasks autonomously with superhuman performance …

To further improve learning efficiency and performance of reinforcement learning (RL), a
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …

Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …

Driver anomaly quantification is a fundamental capability to support human-centric driving
systems of intelligent vehicles. Existing studies usually treat it as a classification task and …

Decision-making for urban autonomous driving is challenging due to the stochastic nature of
interactive traffic participants and the complexity of road structures. Although reinforcement …

To meet rising demands in performance and emissions compliance, companies are driven
to develop systems of ever-increasing complexity. In-the-loop methods use a hybrid …

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 …

Abstract Deep Reinforcement Learning (DRL) models play a fundamental role in
autonomous driving applications; however, they typically suffer from sample inefficiency …

Unmanned Aerial Vehicles (UAVs) can be an important component in the Internet of Things
(IoT) ecosystem due to their ability to collect and transmit data from remote and hard-to …

Reinforcement learning (RL) is a powerful data-driven control method that has been largely
explored in autonomous driving tasks. However, conventional RL approaches learn control …