## 1 Introduction
Decentralized control adopting DRL enables many system operating points to respond in real time with little communication. Therefore, recent efforts have focused on problems in power systems combined with DRL [10; 11; 12; 13; 14; 15]. In several challenging tasks for power system operation, DRL shows great potential. For example, a novel adaptive emergency control scheme using DRL by leveraging the high-dimensional feature extraction and nonlinear generalization capabilities of DRL for complex power systems was proposed [14]. Elsewhere [15], the volt-var optimization algorithm was proposed for reinforcing voltage regulation and power loss reduction. However, the existing voltage control methods using DRL only focus on adjusting voltage profiles without looking into mitigating voltage unbalance and are not flexible enough to adopt changing distribution networks with various newly connected devices and inverter-based resources. In this article, a novel volt-var control strategy for single-phase DG smart inverters for mitigating voltage unbalance targeting the PCC under time-varying operating conditions in a three-phase distribution system is proposed. The problem is extended to the Markov decision process by integrating it with the DRL algorithm. Multiagent proximal policy optimization methods [16] were used to train multiple inverters in a distribution network owing to the stable and fast convergence ability. In the proposed framework, a probability model based on actual PV generations and load profiles is adopted to ensure that the inverter acts properly under the influence of uncertainty in PV outputs. Then, a voltage-passive device, such as the on-load tap-changer (OLTC) and static voltage compensator, is considered as a realistic assumption for a grid. Furthermore, because more inverter-based resources will be added to the grid in the future, an adaptive learning method is needed by using the latest learning models, which are trained using the digital twin model [17]. The digital twin provides an environment for exploration and exploitation for the learning of agents that converges to the optimal solution through various experiences. The proposed volt-var control, which is used by the distribution system operator in control rooms, was predefined through day-ahead training based on forecast PV and load consumption data. Figure 1 shows the structure of updating the model through day-ahead training in data learning centers using digital twins. The procedure for using the developed platform mainly includes two stages: (1) at the training stage, day-ahead training of smart inverters with forecast information about PV generation and load consumption; and (2) at the implementation stage, for using the NN trained on the actual grid, updating the existing NN to the trained one. During the training stage, agents learn an optimal policy through exploration and exploitation and automatically save the best-performance NN parameters. Once the training stage is completed, the agents at the implementation stage use the trained optimal policy to provide optimal control actions to the environment. These efforts are detailed in the following sections. Section 2 shows the voltage unbalance factor at PCC, the volt-var curve and the reactive power capability of each inverter. Section 3 shows the Multiagent DRL Design for Proposed Method, and Section 4 shows the simulation result carried out in the modified IEEE 13-bus system with time-varying load and PV profiles. The conclusion and future work are presented in Section 5.