Multi-agent reinforcement learning (MARL) has long been a significant research topic in
both machine learning and control systems. Recent development of (single-agent) deep …

A plethora of real world problems, such as the control of autonomous vehicles and drones,
packet delivery, and many others consists of a number of agents that need to take actions …

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 …

Despite the increasing interest in multiagent reinforcement learning (MARL) in multiple
communities, understanding its theoretical foundation has long been recognized as a …

Multi-agent reinforcement learning (MARL) has emerged as a useful approach to solving
decentralised decision-making problems at scale. Research in the field has been growing …

Recently, many cooperative distributed multiagent reinforcement learning (MARL)
algorithms have been proposed in the literature. In this work, we study the effect of …

Despite the success of single-agent reinforcement learning, multi-agent reinforcement
learning (MARL) remains challenging due to complex interactions between agents …

This paper deals with distributed reinforcement learning (DRL), which involves a central
controller and a group of learners. In particular, two DRL settings encountered in several …

Reinforcement Learning (RL) is a learning paradigm concerned with learning to control a
system so as to maximize an objective over the long term. This approach to learning has …

Many real-world tasks on practical control systems involve the learning and decision-making
of multiple agents, under limited communications and observations. In this paper, we study …