  Volt-VAR control (VVC) is a critical application in active distribution
network management system to reduce network losses and improve voltage profile.
To remove dependency on inaccurate and incomplete network models and enhance
resiliency against communication or controller failure, we propose consensus
multi-agent deep reinforcement learning algorithm to solve the VVC problem. The
VVC problem is formulated as a networked multi-agent Markov decision process,
which is solved using the maximum entropy reinforcement learning framework and
a novel communication-efficient consensus strategy. The proposed algorithm
allows individual agents to learn a group control policy using local rewards.
Numerical studies on IEEE distribution test feeders show that our proposed
algorithm matches the performance of single-agent reinforcement learning
benchmark. In addition, the proposed algorithm is shown to be communication
efficient and resilient.
