Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training

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.

This article appeared in the news at: https://www.army.mil/article/247261/army_researchers_develop_innovative_framework_for_training_ai