Manifold Learning

Fragment of a discussion from User talk:Xor
← Thread:User talk:Xor/Manifold Learning/reply

Jump to navigation Jump to search

You do not have permission to edit this page, for the following reasons:

The action you have requested is limited to users in the group: Users.
You must confirm your email address before editing pages. Please set and validate your email address through your user preferences.

You can view and copy the source of this page.

I could imagine developing some sort of "LearnedFactor" function that takes as input the firing angle along with the enemy's position, velocity, maybe more complex features like precise MAE, etc. As long as the function is invertible with respect to the firing angle you could then do KNN with those instead of GuessFactors.

Return to Thread:User talk:Xor/Manifold Learning/reply.

The biggest challenge will be how to deal with different settings in recorded and aiming. Guess Factor indeed do this with orbital movement assumption, and PIF with not moving out of wall.

I'm thinking about some end2end deep model, where transformations between recorded and aiming angles can be learnt automatically. E.g. Given a sequence of historical wave intersect location, movement and bullet hits, try to predict the next wave intersect location.

Retrieved from "http://robowiki.net/wiki/Thread:User_talk:Xor/Manifold_Learning/reply#Manifold_Learning_6851"