Here’s a paper by Floreano at EPFL from 1997 explicitly on Red Queen dynamics for creating neural networks for intelligent robot control.
There was lots of discussion of these ideas in the 1990s. In those days we trained very small NNs - tens of nodes - by evolving their weights and topologies. A run could take days on a workstation of the time.
This particular paper is about co-evolving predator and prey, where the behavior of each is the ‘evaluation’ of the other.
Have not read the paper yet, but this not sound like GAN applied to agent training?
Here’s a paper by Floreano at EPFL from 1997 explicitly on Red Queen dynamics for creating neural networks for intelligent robot control.
There was lots of discussion of these ideas in the 1990s. In those days we trained very small NNs - tens of nodes - by evolving their weights and topologies. A run could take days on a workstation of the time.
This particular paper is about co-evolving predator and prey, where the behavior of each is the ‘evaluation’ of the other.
https://infoscience.epfl.ch/entities/publication/a65d0679-68...
OP's link: > Now the researchers have addressed this issue by having both the self-improving agent and the evaluator evolve together.
and your quote:
> This particular paper is about co-evolving predator and prey, where the behavior of each is the ‘evaluation’ of the other.
Both sound like the GAN approach that was popularized a decade ago and kinda the start of the "genAI" boom.
> "Instead of improving an agent against a fixed test, we let the evaluation evolve alongside the agent"
This quote should have been highlighted earlier in the article.
Yeah I think it is broadly applicable to tech as a whole, I mean any great startup is just really a counter positioned company to incumbents -