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This article presents a proximal policy optimization (PPO) basedreinforcement learning (RL) approach for DC-DC boost converter control, whichis compared to traditional control methods. The performance of the PPOalgorithm is evaluated using MATLAB Simulink co-simulation, and the resultsdemonstrate that the most efficient approach for achieving short settling timeand stability is to combine the PPO algorithm with reinforcement learning basedcontrol method. The simulation results indicate that the step responsecharacteristics provided using the control method based on RL with the PPOalgorithm outperform traditional control approaches, which can be used toimprove DC-DC boost converter control. This research also highlights theinherent capability of the reinforcement learning method to enhance theperformance of boost converter control.

Proximal Policy Optimization-Based Reinforcement Learning Approach for  DC-DC Boost Converter Control: A Comparative Evaluation Against Traditional  Control Techniques