I read the 2019 paper[0] where they beat 5 pros in a 6-max table. It did not assume fixed stack sizes, it uses a technique they call "action abstraction" where they train on some number of stack sizes. During a game they use these pre-computed values as starting points for real time searching of the game tree.
Their papers are very well written. With enough determination someone could build a multi player bot.
I was also thinking about trying to build something simpler that works decently well. They published a paper a while back about a bot that only did real time search with no training. It performed better than older poker bots that used to require offline computation. I don't think you'd be able to beat pros with it, but it should do well vs amateurs.
My bad, for some reason assumed you were talking about bet sizes.
Starting stack sizes were static, but I don't think it's something important. They varied during the course of play. If you're playing cash instead of tournament you'd just need to set each player's stack sizes when they join the table.
Their papers are very well written. With enough determination someone could build a multi player bot.
I was also thinking about trying to build something simpler that works decently well. They published a paper a while back about a bot that only did real time search with no training. It performed better than older poker bots that used to require offline computation. I don't think you'd be able to beat pros with it, but it should do well vs amateurs.
[0] https://www.science.org/doi/10.1126/science.aay2400