noisy-bayesian-optimization VS botorch

I spent a long time trying to implement Noisy Baysian Optimization [1], using both standard libraries and my own understanding, but ultimately I never got it to work very well.

It's a real pity, since a smart optimizer for very noisy functions would be really useful. I was trying to use it for chess engine tuning, since I know Deep Mind used it for tuning AlphaZero. I really wonder how they got it to work well.

[1] https://github.com/thomasahle/noisy-bayesian-optimization

The true answer for this is modelling the problem bayesian in the first place using, for example, https://botorch.org/ and https://gpytorch.ai/.

Yes, I'm using a binary outcome, since that's what I get from playing a game. To get probabilities I'd have to play a lot of games with the same settings/features/point and take the mean, but it seems that defeats the point of Bayesian optimization finding the best point to evaluate for each iteration.

The SPSA method seems to work quite well with binary outcomes. This is what I was trying to beat. Unfortunately I was never able to converge faster than SPSA (or even close to that) even increasing the number of samples.

I got some feedback form the botorch team back then: https://github.com/pytorch/botorch/issues/347#:~:text=thomas...