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.

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