Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of
challenging long-horizon decision-making tasks into simpler subtasks. During the past …

Abstract Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning
algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Large sequence models (SM) such as GPT series and BERT have displayed outstanding
performance and generalization capabilities in natural language process, vision and …

In this review, we present an analysis of the most used multi-agent reinforcement learning
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …

Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Unmanned aerial vehicle (UAV)-based wireless networks have received increasing
research interest in recent years and are gradually being utilized in various aspects of our …

A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature
relevance techniques to explain a deep neural network (DNN) output or explaining models …