giuse's comments

Uhm maybe I should have pointed this out earlier but the algorithms implementation can be found (independent of deep neuroevolution) in my Ruby machine learning workbench repo (in turn imported in DNE): https://github.com/giuse/machine_learning_workbench

Uhm unfortunately I do not, I could search for some on Google but I doubt I would fare better than you at it. I went to code my own version, it is quite straightforward. You can find it here: https://github.com/giuse/machine_learning_workbench/blob/mas... although polluted by research's trial and error, you can easily check the minimal code necessary to run. Here's an example of how to use it: https://github.com/giuse/machine_learning_workbench/blob/mas... Let me know if that works for you or if you have further questions!

Sure! It's quite simple: works like a charm. Completely transparent. You `import` with Python-like syntax, and you get a Ruby object that transparently forwards any message (i.e. method calls) to the corresponding Python object on the underlying Python interpreter.

This means that Ruby does not need to know _anything_ about the Python object: whatever you call on the Ruby object is just forwarded to the Python one, and whatever result is passed back to Ruby.

About the overhead, I sincerely do not know; I expected to have some so my code does part of the image pre-processing directly in Python (`narray`) in order to pass a smaller object to Ruby, but besides that I could perceive none -- grain of salt advised, as that was possibly hidden from me because my computation in Ruby was orders of magnitude more complex/time consuming than what was going on on the Python interpreter.

Definitely ping Murata-san either on GitHub https://github.com/mrkn/pycall.rb/ or Twitter `@mrkn`, I will send him a link to this thread so he can contribute if he feels like it. Personally, I am a fan of his work and elegant approach, I owe him for enabling me to keep working in Ruby while everybody publishes code in Python :)

Sure, familiarity matters. But I believe in the rational reasons that brought me back to this tool over and over, until I built familiarity indeed :) I posted an overbloated discussion on it on Reddit, feel free to read as little as you need ;) https://www.reddit.com/r/MachineLearning/comments/8p1o8d/r_p...

You may be interested in an even older paper: http://www.idsia.ch/~juergen/icdl2011cuccu.pdf

Ruby should be perfectly legible with a Python background, but for any question just ping me on twitter. I would be happy to build a dialog :)

Author here. The idea is low-hanging indeed, several friends (including @togelius!) commented "I always wanted to do that -- eventually". Realization is another matter. Have a look at the mess necessary to make it work: we had to discard UL initialization for online learning, accept that the encoding would grow in size, adapt the network sensibly to these changes, and tweak the ES to account for the extra weights.