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arXiv · 1711.00123

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

Will Grathwohl·Dami Choi·Yuhuai Wu·Geoffrey Roeder·David Duvenaud
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Abstract

Gradient-based optimization is the foundation of deep learning and reinforcement learning. Even when the mechanism being optimized is unknown or not differentiable, optimization using high-variance or biased gradient estimates is still often the best strategy. We introduce a general framework for learning low-variance, unbiased gradient estimators for black-box functions of random variables. Our method uses gradients of a neural network trained jointly with model parameters or policies, and is applicable in both discrete and continuous settings. We demonstrate this framework for training discrete latent-variable models. We also give an unbiased, action-conditional extension of the advantage actor-critic reinforcement learning algorithm.

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Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud. 2018-02-23. Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. https://arxiv.org/abs/1711.00123

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