Abstract: Adaptive optimizers are pivotal in guiding the weight updates of deep neural networks, yet they often face challenges such as poor generalization and oscillation issues. To counter these, we ...
Abstract: This paper presents a model-free neural network controller design methodology based on transfer reinforcement learning (TRL) with Gaussian reward shaping, implemented and validated on a Buck ...
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