  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. In addition, multiple chromosomes can define different models, making
it easy to construct multiple models and can be effectively applied to problems
that require multiple models. We show that this G2N can be applied to typical
reinforcement learning algorithms to achieve a large improvement in sample
efficiency and performance.
