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 …

During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …

This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves
the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter …

Agriculture is a vital sector in developing nations such as India, and the use of autonomous
vehicles and Internet of Things (IoT) technology has the potential to revolutionize farming …

The remarkable successes of deep learning in speech recognition and computer vision
have motivated efforts to adapt similar techniques to other problem domains, including …

Small basestations (SBs) equipped with caching units have potential to handle the
unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul …

A variety of Reinforcement Learning (RL) techniques blends with one or more techniques
from Evolutionary Computation (EC) resulting in hybrid methods classified according to their …

Effective network slicing requires an infrastructure/network provider to deal with the
uncertain demands and real-time dynamics of the network resource requests. Another …

It has been recently shown that general policies for many classical planning domains can be
expressed and learned in terms of a pool of features defined from the domain predicates …

NarrowBand Internet of Things (NB-IoT) is an emerging cellular-based technology that offers
a range of flexible configurations for massive IoT radio access from groups of devices with …