[D] AlphaZero for G of GAN • r/MachineLearning

Let's consider a GAN in which G is AlphaZero-like algorithm. Unlike other GAN, G of this one can directly take samples from the dataset as an input in the sense that they can be used in the tree. There are various kinds of RL problems whose evaluation cannot be easily done by a simple algorithm without using a NN. SeqGAN, LeakGAN) is one example, and this is something I'm particularly interested in. Realistic *** generation is a special subset of the above problems in the sense that infinite supply of labelled (in the sense that they are realistic or not) samples are available.