Improved Techniques for Training GANs

> I don't see how game theory is relevant or what insights it provides. The wiki entry of Nash equilibrium [1] and this recent preprint on improving GAN training [2] may help you understand the link between GAN and game theory. To me, this link seems plausible but very vague so far. Training GAN has not directly benefited from the math foundation of game theory. Correct me if I am wrong. [1]: https://en.wikipedia.org/wiki/Nash_equilibrium [2]: https://arxiv.org/abs/1606.03498

They have plenty of results. All their work on GANs[1][2] has been great, and the OpenAI gym has proven to be pretty popular for reinforcement learning research already. I'm not sure how "hype" is measured - perhaps people thought they were going to invent some kind of AI entity we would have to persuade not to shoot us all by now or something. I know that all the papers coming out of OpenAI get hyped just as much as anything from DeepMind, Google Brain or FAIR, so there seems to be decent amount of hype still? But then usually that hype is justified, in which case maybe it is better characterised as "excitement"? [1] http://arxiv.org/pdf/1606.03657.pdf [2] http://arxiv.org/pdf/1606.03498.pdf

Brief summary: a nice intro about what generative models are and the current popular approaches/papers, followed by descriptions of recent work by OpenAI in the space. Quick links to papers mentioned: Improving GANs https://arxiv.org/abs/1606.03498 Improving VAEs http://arxiv.org/abs/1606.04934 InfoGAN https://arxiv.org/abs/1606.03657 Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks http://arxiv.org/abs/1605.09674 Generative Adversarial Imitation Learning http://arxiv.org/abs/1606.03476 I think the last one seems very exciting, I expect Imitation Learning would be a great approach for many robotics tasks.