geoopt VS evotorch

Looking at geoopt/manifolds/stereographic/math.py and trying to learn about hyperbolic geometry as I go, wondering if you could add some commentary to the functions. If anyone is in the mood to teach, I am all ears :). I can prompt with some questions, hopefully that will help clarify what is confusing or hard to understand for a newcomer.

Can you expand on this? What other toolkits already exist and how does yours solve the problems you saw in those frameworks? For example, maybe compare with https://github.com/geoopt/geoopt for one?

population based algos dont care about differentiability https://github.com/google/evojax https://github.com/RobertTLange/evosax https://github.com/nnaisense/evotorch https://github.com/uber-research/PyTorch-NEAT

Check out the release and try it now: https://github.com/nnaisense/evotorch/releases/tag/v0.3.0

EvoTorch (evotorch.ai) makes it straightforward to apply evolutionary reinforcement learning to the challenge. We've included setup help, training and visualisation scripts, a baseline controller trained through the provided script and help for submission to the competition. Simply head to the public GitHub to get started: https://github.com/nnaisense/evotorch-myosuite-starter

An Advanced Evolutionary library or algorithm has been a dream of scientists and AI/ML enthusiasts since the concept was introduced. This vision has come true thanks to the scientists at NNAISENSE, a Switzerland-based AI Enterprise. They created an open-source platform called EvoTorch. When operated in combination with Machine Learning, it can solve complex operational problems in a fraction of time, with lower costs, and at a larger scale. Evolutionary algorithms act as a step toward solving cascading problems that occur when the problem’s size and complexity increase. Evolutionary algorithms make the situations easier to handle the complexity without adding to the cost, they are also much easier to connect through GPUs and CPUs parallelly to ease up the calculation time and the complexity associated with it, that the only limit to your computational power becomes your budget. The evolutionary algorithms are built in the open framework EvoTorch.