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 …

Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …

The Internet of Things (IoT) edge network has connected lots of heterogeneous smart
devices, thanks to unmanned aerial vehicles (UAVs) and their groundbreaking emerging …

In the multiple fields covered by Artificial Intelligence (AI), path planning is undoubtedly one
of the issues that cover a wide range of research lines. To be able to find an optimal solution …

Metaverse is an artificial virtual world mapped from and interacting with the real world. In
metaverse, digital entities coexist with their physical counterparts. Powered by deep …

In recent years, unmanned aerial vehicles (UAVs) have attracted increased attention from
academic and industrial research communities, owing to their wide range of potential …

With the continuous development of UAV technology and swarm intelligence technology, the
UAV formation cooperative mission has attracted wide attention because of its remarkable …

Inefficient traffic control may cause numerous problems such as traffic congestion and
energy waste. This paper proposes a novel multi-agent reinforcement learning method …

We study the multi-agent safe control problem where agents should avoid collisions to static
obstacles and collisions with each other while reaching their goals. Our core idea is to learn …