Hacker News Reader

It would be interesting to see how strength and "human like play" scales with the depth of the search.

There's also a really interesting possibility in training policy networks with different attributes by using games from players with certain styles of play.

View on HN

Training different policies in different styles is a really interesting idea. You could then have a gating process that first chooses the "style" of move to make and then uses the style-specific network to select a move.

I think getting data for this could be difficult though. I wonder how easy it would be to automatically categorize a game record by "style"?

I don't know a lot about chess, but I would try picking several prolific players with what seem to you to be different styles, and training a classifier to identify the player, as an experiment in viability.

Or, rather than multiple policies, one policy that takes a player vector as an input along with the board position. Players that you predict will make the same move from a given board have their vectors adjusted toward each other and away from a random sample of other player vectors.

If it works, you would be able to perform player vector math ala word2vec. (No idea if it will work)

Reply on news.ycombinator.com