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Neural Optimizer Search using Reinforcement Learning
Irwan Bello · Barret Zoph · Vijay Vasudevan · Quoc Le

Tue Aug 08 11:24 PM -- 11:42 PM (PDT) @ C4.5
in Deep reinforcement learning 2 »

We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures. We train a Recurrent Neural Network controller to generate a string in a specific domain language that describes a mathematical update equation based on a list of primitive functions, such as the gradient, running average of the gradient, etc. The controller is trained with Reinforcement Learning to maximize the performance of a model after a few epochs. On CIFAR-10, our method discovers several update rules that are better than many commonly used optimizers, such as Adam, RMSProp, or SGD with and without Momentum on a ConvNet model. These optimizers can also be transferred to perform well on different neural network architectures, including Google’s neural machine translation system.

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Author Information

Irwan Bello (Google Brain)
Barret Zoph (Google)
Vijay Vasudevan (Google)
Quoc Le (Google Brain)

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