The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices
around the world, where the IoT devices collect and share information to reflect status of the …

Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

Deep reinforcement learning (DRL) integrates the feature representation ability of deep
learning with the decision-making ability of reinforcement learning so that it can achieve …

A promising characteristic of Deep Reinforcement Learning (DRL) is its capability to learn
optimal policy in an end-to-end manner without relying on feature engineering. However …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Abstract The 5th Generation (5G) and beyond networks are expected to offer huge
throughputs, connect large number of devices, support low latency and large numbers of …

In this survey, we systematically summarize the current literature on studies that apply
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …

Human drivers have different driving styles, experiences, and emotions due to unique
driving characteristics, exhibiting their own driving behaviors and habits. Various research …

Due to the increasing use of machine learning in practice it becomes more and more
important to be able to explain the prediction and behavior of machine learning models. An …