Driver behavior, vehicle dynamics, and operating conditions strongly influence fuel consumption in open-pit mining operations. This study proposes a driver behavior–aware fuel optimization framework that integrates a digital twin architecture with reinforcement learning to improve fuel efficiency of heavy-duty haul trucks. The framework combines a data-driven vehicle dynamics surrogate, explicit modeling of driver behavior, and proximal policy optimization to enable safe and scalable policy learning within a realistic simulation environment. Historical telematics data were used to construct the digital twin and evaluate the learned policy under controlled operating conditions. Experimental results show that the reinforcement learning agent produces substantially smoother driving behavior, characterized by stable speed regulation and elimination of aggressive acceleration and braking events. Compared to historical operator driving, fuel consumption per kilometer, computed using rollout-level aggregation of cumulative fuel consumption and total traveled distance, was reduced from 4.45 L/km to 3.02 L/km, corresponding to a 32.05% improvement in fuel efficiency. The results demonstrate that explicitly modeling driver behavior within a digital twin-based reinforcement learning framework can yield significant fuel savings while maintaining realistic and interpretable driving strategies. The proposed approach provides a promising foundation for the development of decision-support and driver assistance systems aimed at improving energy efficiency in open-pit haulage operations.