Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training
Multiagent Systems
2023-07-26 v1 Artificial Intelligence
Machine Learning
Abstract
Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collaboration in cooperative tasks. Here, we discuss variations of centralized training and describe a recent survey of algorithmic approaches. The goal is to explore how different implementations of information sharing mechanism in centralized learning may give rise to distinct group coordinated behaviors in multi-agent systems performing cooperative tasks.
Cite
@article{arxiv.2107.14316,
title = {Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training},
author = {Piyush K. Sharma and Rolando Fernandez and Erin Zaroukian and Michael Dorothy and Anjon Basak and Derrik E. Asher},
journal= {arXiv preprint arXiv:2107.14316},
year = {2023}
}
Comments
This article appeared in the news at: https://www.army.mil/article/247261/army_researchers_develop_innovative_framework_for_training_ai