The impact of internal combustion engine-powered automobiles on climate change due to
emissions and the depletion of fossil fuels has contributed to the progress of electrified …

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

The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

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 …

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 …

Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

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 …

Interaction is fundamental in autonomous driving (AD). Despite more than a decade of
intensive R&D in AD, how to dynamically interact with diverse road users in various contexts …