https://arxiv.org/abs/1711.00123
by Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
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 for VAE Experiments lives here. The Discrete RL experiments can be found at: https://github.com/wgrathwohl/BackpropThroughTheVoidRL.
A simplified, pure-python implementation is in /relax-autograd/relax.py
If you have any questions about the code or paper please contact Will Grathwohl (wgrathwohl@cs.toronto.edu). The code is in "research-state" at the moment and I will be updating it periodically. If you have questions feel free to email me and I will do my best to respond. -Will