In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

This article focuses on the recent advances in the field of reinforcement learning (RL) as well
as the present state–of–the–art applications in games. First, we give a general panorama of …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Reinforcement learning is considered to be a strong AI paradigm which can be used to
teach machines through interaction with the environment and learning from their mistakes …

There is currently a huge interest around autonomous vehicles from both industry and
academia. This is mainly due to recent advances in machine learning and deep learning …

The idea of using evolutionary computation to train artificial neural networks, or
neuroevolution (NE), for reinforcement learning (RL) tasks has now been around for over 20 …

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 …

With the development of state-of-art deep reinforcement learning, we can efficiently tackle
continuous control problems. But the deep reinforcement learning method for continuous …

Most methods that attempt to tackle the problem of Autonomous Driving and overtaking
usually try to either directly minimize an objective function or iteratively in a Reinforcement …

Autonomous vehicles have received considerable attention, yet high-speed path following
control remains a critical and challenging issue. At high speeds, achieving perfect control …