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Parameter Design Optimization for DC-DC Power Converters with Deep Reinforcement Learning

The deep deterministic policy gradient (DDPG) algorithm, a reinforcement learning (RL) technique which trains an agent to achieve the maximal reward by interacting with the environment, is applied for parameters optimization for a DC-DC power converter. A model for evaluating the efficiency of both semiconductor and magnetic components is developed. An artificial neural network (ANN) is trained by data from Spice simulation to establish a fast and accurate model for semiconductor losses. In the meantime, magnetic design is conducted by considering various core shapes, materials, winding turns, and airgaps. Finally, DDPG is trained by the combined efficiency evaluation model. The results indicate that the ANN model for semiconductor losses is accurate and the optimal design is obtained quickly by the trained DDPG algorithm.