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 …

Due to the limited smartness and abilities of machine intelligence, currently autonomous
vehicles are still unable to handle all kinds of situations and completely replace drivers …

Deep reinforcement learning (DRL) is a machine learning method based on rewards, which
can be extended to solve some complex and realistic decision-making problems …

When autonomous vehicles are deployed on public roads, they will encounter countless and
diverse driving situations. Many manually designed driving policies are difficult to scale to …

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

Reinforcement learning (RL) requires skillful definition and remarkable computational efforts
to solve optimization and control problems, which could impair its prospect. Introducing …

In this paper, we present a safe deep reinforcement learning system for automated driving.
The proposed framework leverages merits of both rule-based and learning-based …

Due to its limited intelligence and abilities, machine learning is currently unable to handle
various situations thus cannot completely replace humans in real-world applications …

Deep reinforcement learning has achieved great progress recently in domains such as
learning to play Atari games from raw pixel input. The model-free characteristics of …

Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to
its potential to solve complex classification and control problems. However, existing RL …