Comments (3)
Original comment by Marius Lindauer (Bitbucket: mlindauer, GitHub: mlindauer):
untouched since some month and nobody really complained ... so won't fix for the time being
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Original comment by Marius Lindauer (Bitbucket: mlindauer, GitHub: mlindauer):
Hi Tobi,
I see your point.
However, I would prefer to not change this.
If you use SMAC with the option algo-deterministic=false, SMAC will automatically generate random seed for your target algorithm runs. You don't need to use these seeds if you don't want, but still the runhistory can store the results properly.
Cheers,
Marius
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Original comment by Tobias Springenberg (Bitbucket: stokasto, GitHub: stokasto):
Right, if the seed is set everywhere then that would work, still in our RL experiments for example we have to interact with real robots and runs of the same parameters are not necessarily deterministic, there is noise in the real world and noise in the returned function values ...
Also AFAIK a lot of the "synthetic" benchmarks used in bayesian optimization are noisy functions and I guess you want to be able to optimize these.
Cheers,
Tob
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