Deep reinforcement learning has recorded remarkable performance in diverse application
areas of artificial intelligence: pattern recognition, robotics, object segmentation …

Reinforcement learning (RL) has been successful in training agents in various learning
environments, including video games. However, such work modifies and shrinks the action …

Reinforcement learning (RL) combined with deep neural networks has performed
remarkably well in many genres of games recently. It has surpassed human-level …

Reinforcement learning (RL) applications require a huge effort to become established in real-
world environments, due to the injury and break down risks during interactions between the …

Recently, as UAVs (unmanned aerial vehicles) have become smaller and higher-
performance, they play a very important role in the Internet of Things (IoT). Especially, UAVs …

In this study, an end‐to‐end noninvasive frame system available for varieties of complete
information games was first implemented. After altering some codes, the system can be …

Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) are efficient algorithms
for video game artificial intelligence (AI) agents, while Supervised Learning (SL) would …

Reinforcement learning (RL) is one of three basic machine learning paradigms, alongside
supervised learning and unsupervised learning. Reinforcement learning algorithms have …

[en] Machine learning is an ever-expanding field of research with a wide range of potential
applications. It has been increasingly used in different robotics tasks enhancing their …

The Internet of Things with its discovery for linking billions of devices and static devices to
aid with numerous applications in real time has make cloud computing paradigms encounter …