Sample efficiency is a critical metric in intelligent control systems as it directly influences the feasibility and effectiveness of learning-based approaches. This paper presents the study of how Randomized Ensemble Double Q-Learning (REDQ), a sample-efficient model free algorithm, can be used in flight control applications. Three controllers were developed for: pitch, roll and combined biaxial attitude tracking tasks and tested on a high fidelity Cessna Citation 550 model. For each control task, three agents were trained offline: two using REDQ enhanced Soft Actor Critic (SAC) architectures and one using a standard SAC architecture for comparison. REDQ agents showed statistically significant improvements in sample efficiency during initial learning. Average accurate tracking convergence (error < 1◦) occurred within 5,500 training steps for pitch, 6,400 for roll and 11,500 for biaxial control. The gains in sample efficiency were shown to have drawbacks in learning stability and robustness when deviated too far from nominal conditions.