Existing deep reinforcement learning (RL) are devoted to research applications on video
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …

In real-world scenarios, making navigation decisions for autonomous driving involves a
sequential set of steps. These judgments are made based on partial observations of the …

Deep reinforcement learning (DRL) has achieved great success in processing vision-based
driving tasks. However, the end-to-end training manner makes DRL agents suffer from …

Vision-based autonomous urban driving in dense traffic is quite challenging due to the
complicated urban environment and the dynamics of the driving behaviors. Widely-applied …

Deep reinforcement learning (DRL) provides a promising way for learning navigation in
complex autonomous driving scenarios. However, identifying the subtle cues that can …

Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …

Driving through complex urban environments is a challenging task for autonomous vehicles
(AVs), as they must safely reach their mission goal, and react properly to traffic participants …

Human drivers have excellent perception and reaction abilities in complex environments
such as dangerous highways, busy intersections, and harsh weather conditions. To achieve …

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 …

Despite some successful applications of goal-driven navigation, existing deep reinforcement
learning (DRL)-based approaches notoriously suffers from poor data efficiency issue. One of …