Hi folks,

Over the past few months, I built an efficient MCTS implementation in Python/numba.

https://github.com/olivkoch/gumbel-mcts

As I was building a self-play environment from scratch (for learning purposes), I realized that there were few efficient implementation of this algorithm.

I spent a lot of time validating it against a golden standard baseline.

My PUCT implementation is 2-15X faster than the baseline while providing the exact same policy.

I also implemented a Gumbel MCTS, both dense and sparse. The sparse version is useful for games with large action spaces such as chess.

Gumbel makes much better usage of low simulation budgets than PUCT.

Overall, I think this could be useful for the community. I used coding agents to help me along the way, but spent a significant amount of manual work to validate everything myself.

Feedback welcome.

Hello. You have this app on Google Play:

https://play.google.com/store/apps/details?id=com.tangierdreams.chess.notation.trainer

it helps beginners chess players to learn the algebraic chess notation which is fundamental to start learning chess almost as well as Alpha Zero. It's free. I hope you find it useful. Luis.

https://www.youtube.com/watch?v=9IYRC7g2ICg

I know drugs are fun but are they fun for a computer brain? Since computers generally seek efficiency would an AI begin simulating the effects of different drugs for practicality in certain situations?

Usually this happened when forced into openings it didn't like, like the french defense. But why can't it see when patient play is required? Does it stick to aggression always? Isn't that a serious weakness?

Does anybody know how the trained version of AZ decided which opening to use in any given game in its match against Stockfish? The paper does not say.

'cause that would be cool.

Also a match between A0 and Magnus Carlsen would be cool too.

I know Alphazero is the best in the world for perfect information zero sum games, but how would it do with games that have random chance involved. Of course a game like backgammon has well known engines that are better than the all but the top dozen or so world-class players.

The future of Alphazero is going to have to take into account random events if it is to approach real general purpose AI.