[eminence32]

This is obviously just a research project, but I do wonder about the next steps:

* After exploring an learning about a virtual world, can anything at all be transferred to an agent operating in the real world? Or would an agent operating in the real world have to be trained exclusively or partially in the real world?

* These virtual worlds are obviously limited in a lot of important ways (for example, character locomotion in a game is absolutely nothing like how a multi-limbed robot moves). Does there eventually need to be more sophisticated virtual worlds that more closely mirror our real world?

* Google seems clearly interested in generalized agents and AGI, but I'm actually somewhat interested in AI agents in video games too. Many video games have companion NPCs that you can sort of give tasks to, but in almost all cases, the companion NPCs are nearly uncontrollable and very limited in what they can actually do.

[nharada]

The end goal is to marry the lessons learned about HOW to learn in a virtual world with a high fidelity world model that's currently out of reach for this generation of AI. In a year or two once we have a world model that's realistic enough and fast enough, robots will be trained there and then (hopefully) generalize easily to the real world. This is groundwork trying to understand how to do that without having the models required to do it for real.

[mkoubaa]

Look into the sim2real problem in robotics