7 ms·
Wow, that's really unexpected. This isn't my field, but given the simplicity of the inputs and network, and the way commenters are seeing the demo achieve perf
by fenomas 10y ago
Wow, that's really unexpected.
This isn't my field, but given the simplicity of the inputs and network, and the way commenters are seeing the demo achieve perfect play after anything between 2 and 200 generations, it makes me wonder if this isn't more of a brute-force search than actual learning?
That is, it smells like there's a "correct" set of neuron values - where any genome within some tolerance of those values wins forever, and any other genome dies quickly. If that's the case, the system can't really evolve towards a solution, can it? It would just cycle randomly through lots of genomes that die immediately, until by pure chance one lives forever. I only tried the demo a few times but that's what it looked like it was doing.
- maaaats 10y ago"All" evolutionary algorithms are basically a local search with smart heuristics. Where a local search is a brute-force where you move in small directions based on feedback on where you are in the solution space.
- fenomas 10y agoI understand how the algorithms work. What I'm suggesting is that the demo seems to behave like a hill-climbing algorithm that's been unleashed on a terrain that's flat everywhere except the solution.
- maaaats 10y agoAh, I see what you mean now. Yeah, the problem space seems a bit simple. I never see any "learning" before it suddenly achieves perfect play.
- raverbashing 10y agoNot really, if you add new individuals from random then you're doing some global search (or just have a higher mutation rate - but that has some problems)
- deleted 10y ago[deleted]