patricia devlog

[pohl4711]

I definitely understand this for max depth (and will come back around to revising Patricia's skill levels before releasing), but for endgames why does it need more nodes? Thanks to the transposition table engines can hit very high depths with comparatively very few nodes.

OK, this might be true. I referred to TheKing-Chesscomputer, which only has less than 400 Kilobytes for Hashtables...

[Whiskers]

I decided to extract data from SPCC testing to get some better data for retraining Patricia's net on. To do this, I grabbed Patricia's games, as well as all the games played in SPCC testing (found on the site), used the interesting wins filter to search for, well, interesting games, used pgn-extract to grab the FENs (with best moves and scores) from the PGNs, and wrote a script to perform filtering + conversion on those FENs. This yielded about 8.25m "interesting" FENs; if retraining Patricia's network on it yields positive results, I'll probably grab CCRL games as well.

For testing the new retrained net I'm going to remove the features that directly force sacrifices in Patricia. I feel like they're a bit unhealthy for how she plays, especially as the bonuses get *huge* for some sacrifices. I think I'm also not going to let Patricia give bonuses for sacrifices if she's losing, because sacrifices in losing positions are really just throwing pieces in the garbage and are not conducive whatsoever to style of play.

After several games, I've come to the conclusion that this isn't working. However I'm finding promise with a new filtering script for Willow data. Once I maximize style and minimize strength loss I'll make a post about it!
I also think I swatted another bug. This one was brought to my attention by a GitHub issue and apparently the code that breaks it only fails in rare circumstances at the root node. I still don't understand why none of these bugs cause Patricia to crash on my machine.

[Whiskers]

Progress is still somewhat slow. I got a decent net that sacrifices at the same rate as Komodo on its aggressive setting with no other aggression changes, but am having trouble improving on it.

One thing I've noticed is how thin the line to tread in terms of learning rate is. I've had nets where going from a learning rate of 0.000001 to 0.0000025 has no effect on ELO, but going from 0.0000025 to 0.000005 instantly loses 200 or more ELO. So for each net I usually have to spend several runs just zeroing in on the best LR that maximizes the net's response to retraining without killing its strength.