Advances in reinforcement learning research have recently produced agents that are
competent, or sometimes exceed human performance, in complex tasks. Most interesting …

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 …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Introduction: Multi-agent systems are an interdisciplinary research field that describes the
concept of multiple decisive individuals interacting with a usually partially observable …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper
presents a comprehensive survey of MARL and its applications. We trace the historical …

Multi-agent reinforcement learning (MARL) has achieved promising results in recent years.
However, most existing reinforcement learning methods require a large amount of data for …

Multi-agent reinforcement learning (MARL) is a widely researched technique for
decentralised control in complex large-scale autonomous systems. Such systems often …