[kevinflo]

I'm a layman, but to my knowledge navigating a digital environment and a real one are the same minus some steps of the process. A self-driving car recreates a digital reality via sensors with as little delay and fidelity loss as possible, then navigates a digital car within that rapidly constructed virtual reality. It then signals back to the real car to navigate the real car exactly how it would navigate the digital one given its immediate digital environment. Since many of the hard problems lie with the navigation in the digital space, removing the sensors-to-digital part of it by training with video games still nets a lot of valuable learnings that can be brought right back into real-world applications. Also, learning in a fully digital environment allows this part of the learning to be done without the spatial/time constraints of reality.

[buboard]

games are simulations of idealized processes using generally a small number of equations. a sufficient neural network should be able to learn them but the real world has orders of magnitude higher complexity. Something similar happens with robots trained with physics engines.