Deep Reinforcement Learning for DC-DC Converter Parameters Optimization

Reinforcement learning (RL) is a type of machine learning in which an agent teaches itself by interacting with the environment. A RL-based parameter optimization method is proposed to improve the efficiency of a DC-DC power converter. More specifically, deep Q network (DQN) methods are utilized to optimize the power converter's parameter designs under current, voltage ripple, and volume limitations. Spice simulation is used to determine power losses on semiconductors. Combined with an optimal design of the inductor, the overall efficiency of power converters is obtained. The results show that an optimal design is obtained by using the DQN algorithm.