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High penetration of rooftop photovoltaic generation introduces rapid voltage fluctuations in low-voltage distribution feeders. These fluctuations challenge conventional voltage regulation because inverter actions must respond locally while remaining coordinated across electrically coupled nodes. This article proposes a multi-agent reinforcement learning model for grid-edge voltage regulation under distributed PV variability and communication latency. The objective is to maintain feeder voltages within operational limits while reducing unnecessary reactive power support, curtailment, and communication burden. The voltage control task is formulated as a decentralized partially observable Markov decision process. Smart inverter and battery agents use local observations during execution, while centralized training uses feeder-wide voltage, PV, and control information to train coordinated policies. Simulation results show that the proposed MARL controller improves voltage compliance relative to local Volt/VAr control and approaches centralized optimal power flow performance. The controller remains stable under bounded latency and asynchronous agent updates. The proposed framework offers a robust decentralized control strategy for high-PV distribution feeders. It combines grid-edge autonomy with coordinated learning, making it suitable for communication-limited voltage regulation.