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Deep Reinforcement Learning for DC-DC converter parameters optimization

Reinforcement learning is a machine learning approach where an agent trains itself by interacting with its environment. A based parameters optimization method for reinforcement learning (RL) is proposed to improve the efficiency of a DC-DC power converter. More specifically, deep Q network (DQN) algorithms are implemented to search for optimal parameter designs of the power converter under constraints of volume, current, and voltage ripples. The preliminary results show that an optimal design is obtained by using the DQN algorithm.