AAAI 2026technical0 citations
SDE-HARL: Scalable Distributed Policy Execution for Heterogeneous-Agent Reinforcement Learning
Toan D. Gian, Mohammad Abdi, Nathaniel D. Bastian, Francesco Restuccia
Abstract
HARL enables agents to execute cooperative tasks by adopting agent-specific policies. Most of existing HARL methods use individual policy neural networks to ensure monotonic improvement, which leads to substantial computational overhead. The proposed SDE-HARL overcomes this limitation by decomposing each agent
BibTeX
@inproceedings{aaai2026_sdeharlscalabled,
title = {SDE-HARL: Scalable Distributed Policy Execution for Heterogeneous-Agent Reinforcement Learning},
author = {Toan D. Gian and Mohammad Abdi and Nathaniel D. Bastian and Francesco Restuccia},
booktitle = {AAAI 2026},
year = {2026}
}