As small modular reactors move toward multi-module operation, the increased cognitive workload on operators-particularly power-increase operation. This study develops an autonomous algorithm to automate the power-increase operation. To achieve this, this study first performed a task analysis of the power-increase operation to identify candidate tasks for automation and to derive automation strategies. Based on the task analysis results, an algorithm was designed by combining a deep reinforcement learning-based system with a rule-based system, specifically proximal policy optimization and if-then rules, respectively. Experimental results demonstrated that the algorithm successfully achieved 100% reactor power while maintaining safety constraints, such as keeping the startup rate below 0.5 dpm. Additionally, the developed algorithm was implemented and visualized through a graphical user interface.