Genetic-Gated Networks for Deep Reinforcement Learning

NeurIPS 2018 Simyung ChangJohn YangJaeseok ChoiNojun Kwak

We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks. Our method can take both advantages of gradient-free optimization and gradient-based optimization methods, of which the former is effective for problems with multiple local minima, while the latter can quickly find local minima... (read more)

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