This document discusses generative adversarial networks (GANs) and their relationship to reinforcement learning. It begins with an introduction to GANs, explaining how they can generate images without explicitly defining a probability distribution by using an adversarial training process. The second half discusses how GANs are related to actor-critic models and inverse reinforcement learning in re
![(DL hacks輪読) Variational Dropout and the Local Reparameterization Trick](https://cdn-ak-scissors.b.st-hatena.com/image/square/11accee24292fb8b5bd4d3654d76d6251f6ee266/height=288;version=1;width=512/https%3A%2F%2Fcdn.slidesharecdn.com%2Fss_thumbnails%2F20150717-suzuki-160226124140-thumbnail.jpg%3Fwidth%3D640%26height%3D640%26fit%3Dbounds)