There have been a few attempts at communicating deep learning methodology for procedural generation specifically within the roguelike community to use a specific example. While there is typically some high-level interest, the interest quickly fades and developers go back to using traditional procgen methods.

One of the hangups that I've observed is that many of the introductions for procgen deep learning (and deep learning in general) use SL, USL, and AL which require datasets containing many examples. The (time) cost associated with gathering or creating these examples is not appealing to procgen devs. Procgen devs continue using the procgen equivalent of symbolic AI. RL in procgen is largely avoided for the same reasons that RL is rare in other domains.

Despite this, I believe there is a connection between the fields through the lens of optimization problems. Typically procgen practitioners have a handful of parameters which they hand-tune to arrive at their desired results. The number of parameters is kept low so as not to exceed the cognitive capabilities of the practitioner. I'm a believer that by turning many of the current discrete procgen algorithms into continuous-valued generators, the number of generator parameters can be increased a thousand or million-fold so long as an appropriate loss function can be crafted (in practice this isn't very hard and proxies can even be used in tricky cases). In a lot of ways this becomes a reparameterization in a way that makes for more salient generator parameters.

For me one path forward is crafting continuous versions of existing discrete generators and using autodiff tools like JAX to optimize procgen parameters. This whole rant is pretty specific to the roguelike domain and probably doesn't carry over well to other spaces. Huge YMMV disclaimer.