Skip to main content
Cooperative multi-agent actor-critic for privacy-preserving load scheduling in a residential microgrid
  • Zhaoming Qin, Tsinghua University, Beijing, China
  • Nanqing Dong, University of Oxford, United Kingdom
  • Eric P. Xing, Carnegie Mellon University, United States & Mohamed bin Zayed University of Artificial Intelligence
  • Junwei Cao, Tsinghua University, Beijing, China
Document Type

As a scalable data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving the cooperative residential load scheduling problems. However, the common centralized training strategy of MARL algorithms raises privacy risks for involved households. In this work, we propose a privacy-preserving multi-agent actor-critic framework where the decentralized actors are trained with distributed critics, such that both the decentralized execution and the distributed training do not require the global state information. The proposed framework can preserve the privacy of the households while simultaneously learn the multi-agent credit assignment mechanism implicitly. The simulation experiments demonstrate that the proposed framework significantly outperforms the existing privacy-preserving actor-critic framework, and can achieve comparable performance to the state-of-the-art actor-critic framework without privacy constraints. Copyright © 2021, The Authors. All rights reserved.

Publication Date
  • Cryptography and Security (cs.CR),
  • Machine Learning (cs.LG); Multiagent Systems (cs.MA)

IR Deposit conditions: non-described

Preprint: arXiv

Citation Information
Z. Qin, N. Dong, E. Xing, and J. Cao, "Cooperative multi-agent actor-critic for privacy-preserving load scheduling in a residential microgrid", arXiv, Oct. 2021, doi: 10.48550/arXiv.2110.02784