Hopefully can be made trainable then it will be first (to our knowledge) physically realizable autoregressive transformer-decoder-ish-ly-kinda-maybe model.
Screen.Recording.2025-05-12.at.5.01.24.PM.mov
ML
- perplexity? / zip compression for randomness? run_sim but dont print choices, print perplexity etc instead
- grad and train
- force prefix
ML / physical? problems:
- low perplexity, BOO state is 50% L/R, and looks like ball will almost never fall far from init pos
Bugs
- some weird step bug: python pboard.py --force-np-random -r -n2 -i 0.0
- -vvv breaks render
Refactor
- logical sim, disentangle from debug/render
- -rr render should show "sampling mask" and "physically achievale states"
Physical board
- boundaries
- multilayer board
- switch layers from off boundaries
- or just "drop down on next layer" essentially "holes". How to make them trainable?
- some "jump forward" holes too?