MESA: Cooperative Meta-Exploration in Multi-Agent Learning through Exploiting State-Action Space Structure

Multi-agent reinforcement learning (MARL) algorithms often struggle to find strategies close to Pareto optimal Nash Equilibrium, owing largely to the lack of efficient exploration. The problem is exacerbated in sparse-reward settings, caused by the larger variance exhibited in policy learning. This...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Zhang, Zhicheng, Liang, Yancheng, Wu, Yi, Fang, Fei
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Learning Computer Science - Multiagent Systems
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!