Control problems are always challenging since they arise from the real-world systems where stochasticity and randomness are of ubiquitous presence.

While existing DML attempts have made significant progress, two technical barriers remain in realistic scenarios.

An alternative approach, Generative Adversarial Networks (GANs), has become popular across severaldomains, particularly Computer Vision, owing tobreakthrough realism intheimages they output[e.g.,19,65]. This is the case in NLP where, unlike computer vision, a measure of likelihood called perplexityhas been theprevailing metric fortraining and evaluating language models fordecades.