we have used this toolbox as an example in our application (https://github.com/upb-lea/openmodelica-microgrid-gym), where we will design a current controller (standard PI; Kp/Ki) for an inverter.

expected behavoiur

When fliping the dimensions of our two dimensional optimization space, we would expect the ploted results should only flip the axis.

Observed behavior

However, when observing the point marked in green, the values change!

what we investigated

bounds are flipped accourdingly

Ideas

We have the impression, that the first dimension is explored much better than the second dimension. in the left plot the y values never exeed 0.06 even though they are deemed save in the right experiment. Similarly in the right plot, it never dares to try above 150, but in the left it is explored and also save. Therefore we assume it is related to the handling of expanders.

Do you have any hints on how to investigate it further. We are not certain that it is a bug, but the anti-symmetry seems odd.

Hi. Your algorithm is pretty good and it's very useful in many cases. But,there is an error:"TypeError: object of type 'numpy.float64' has no len()." when i try to change plot_3d=False to True in 2d_example.n It's not a big matter in Jupyter notebook(In jupyter it can still display the 3D function images),but it is unexecutable in Pycham. Do you know how to make it? Thanks.

Hi. I wonder whether it is possible to add the upper bound constraints, i.e., gps<=fmax, which are opposite to the lower bound constraints, i.e., gps>=fmin. It seems to be possible to add the upper bound constraints to the "comput_safe_set" functioon when lipschitz constant L is infinite.

With Python 3.10 Sequence was moved to collections.abc. This PR ensures that the library can be used for Python < 3.10 as well as for Python >= 3.10.

Hi there!

I'm trying to replicate the result of SafeOpt on the MovieLen 100k dataset and use it as a baseline for my class project. I tried to follow the procedure described in the paper but I failed to reproduce the result. Can I get some help? Thank you!

Here are the steps that I followed:

And here is my code:

The problem right now is that

• the algorithm breaks the safety constraint pretty quick (explored movies that have a 1.0 rating)
• the algorithm tends to be stuck at one or multiple high rating recommendations after some iterations and it is not able to generate novel results.
• similar to the second point, it explores only a small portion of the reachable set

We experimented with different std and noise variance but did not actually make significant improvement. I wonder do you notice any mistake we made in our code? In addition, would it be possible to share the code you used for the evaluation in the paper?

Thank you very much!

(Only boundaries for GP changed in the 2 plots) Do you have any hints how a change in the boundaries effect exploration that much? Our understanding was, that boundaries shouldn't effect exploration at all but only act as a limit.