Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a
large number of domains, including railways. In this paper, we present a systematic literature …

Robot navigation in crowded public spaces is a complex task that requires addressing a
variety of engineering and human factors challenges. These challenges have motivated a …

Model-free reinforcement learning (RL) can be used to learn effective policies for complex
tasks, such as Atari games, even from image observations. However, this typically requires …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Immersive virtual reality (VR) video is becoming increasingly popular owing to its enhanced
immersive experience. To enjoy ultrahigh resolution immersive VR video with wireless user …

We propose a distributed architecture for deep reinforcement learning at scale, that enables
agents to learn effectively from orders of magnitude more data than previously possible. The …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

Robots that navigate among pedestrians use collision avoidance algorithms to enable safe
and efficient operation. Recent works present deep reinforcement learning as a framework …

Mobile-edge computing (MEC) is a promising paradigm to improve the quality of
computation experience of mobile devices because it allows mobile devices to offload …

Deep reinforcement learning is a promising approach to learning policies in uncontrolled
environments that do not require domain knowledge. Unfortunately, due to sample …