Decentralized Multi-agent Reinforcement Learning With Networked Agents: Recent Advances

Abstract

Multi-agent reinforcement learning (MARL) has long been a significant and everlasting research topic in both machine learning and control. With the recent development of (single-agent) deep RL, there is a resurgence of interests in developing new MARL algorithms, especially those that are backed by theoretical analysis. In this paper, we review some recent advances a sub-area of this topic: decentralized MARL with networked agents. Specifically, multiple agents perform sequential decision-making in a common environment, without the coordination of any central controller. Instead, the agents are allowed to exchange information with their neighbors over a communication network. Such a setting finds broad applications in the control and operation of robots, unmanned vehicles, mobile sensor networks, and smart grid. This review is built upon several our research endeavors in this direction, together with some progresses made by other researchers along the line. We hope this review to inspi

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyzhang2019decentralized

Related papers

• Fully Decentralized Multi-agent Reinforcement Learning With Networked Agents (2018)0.00
• A Review Of Cooperative Multi-agent Deep Reinforcement Learning (2019)19.08
• Mean-field Multi-agent Reinforcement Learning: A Decentralized Network Approach (2021)0.00
• Multi-agent Reinforcement Learning In Stochastic Networked Systems (2020)0.00
• Deep Multiagent Reinforcement Learning: Challenges And Directions (2021)0.00
• Multi-agent Reinforcement Learning: A Selective Overview Of Theories And Algorithms (2019)21.85
• Model-based Multi-agent Reinforcement Learning: Recent Progress And Prospects (2022)0.00
• An Initial Introduction To Cooperative Multi-agent Reinforcement Learning (2024)0.00