Rapid integration of advanced sensors onto legacy military aircraft is critical for maintaining technological advantage in warfighting domains. Integration of these sensors is accomplished through upgrade programs that often fail during integration due to defect discovery and interoperability issues. Existing Department of Defense initiatives related to open architectures have improved sensor integration but have not eliminated the need for custom interface software to account for behavioral disparities across different sensors. The subject research proposes that reinforcement machine learning algorithms can be applied to aircraft sensor interfaces during integration and verifies effectiveness by training and testing Greedy, Q‐Learning, Deep Q‐Learning, Double Deep Q‐Learning, and Instance‐Based Learning algorithms against modeled Global Positioning System (GPS), Optical, Light Detection and Ranging (LIDAR), and Infrared sensor functions. The results are useful to open architecture standards management groups, sensor vendors, and systems and software engineers who are developing strategies and designs to accelerate subsystem integration timelines by reducing failures discovered during integration.