Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents
Machine Learning
2020-06-24 v1 Artificial Intelligence
Multiagent Systems
Systems and Control
Systems and Control
Abstract
This article reviews recent advances in multi-agent reinforcement learning algorithms for large-scale control systems and communication networks, which learn to communicate and cooperate. We provide an overview of this emerging field, with an emphasis on the decentralized setting under different coordination protocols. We highlight the evolution of reinforcement learning algorithms from single-agent to multi-agent systems, from a distributed optimization perspective, and conclude with future directions and challenges, in the hope to catalyze the growing synergy among distributed optimization, signal processing, and reinforcement learning communities.
Cite
@article{arxiv.1912.00498,
title = {Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents},
author = {Donghwan Lee and Niao He and Parameswaran Kamalaruban and Volkan Cevher},
journal= {arXiv preprint arXiv:1912.00498},
year = {2020}
}