Keywords: optimization, machine learning, variational inference, reinforcement learning, gradient estimation, deep learning, discrete optimization
TL;DR: We present a general method for unbiased estimation of gradients of black-box functions of random variables. We apply this method to discrete variational inference and reinforcement 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.
Code: [![github](/images/github_icon.svg) duvenaud/relax](https://github.com/duvenaud/relax) + [![Papers with Code](/images/pwc_icon.svg) 6 community implementations](https://paperswithcode.com/paper/?openreview=SyzKd1bCW)
Data: [OpenAI Gym](https://paperswithcode.com/dataset/openai-gym)