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

Research paper by Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud

Indexed on: 31 Oct '17Published on: 31 Oct '17Published in: arXiv - Computer Science - Learning



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, based on gradients of a learned function. These estimators can be jointly trained with model parameters or policies, and are applicable in both discrete and continuous settings. We give unbiased, adaptive analogs of state-of-the-art reinforcement learning methods such as advantage actor-critic. We also demonstrate this framework for training discrete latent-variable models.