From what I’ve seen, it really depends on what you’re aiming for. Self-play usually helps the bot find optimal strategies over time, but the downside is that it can make the style hard to recognize for everyday players. On the other hand, human data brings that realistic touch, since people don’t always make “perfect” moves. I’ve read a breakdown on Poker bot that showed how combining both approaches actually creates a more balanced system — kind of the best of both worlds. Personally, when I tried testing a bot that leaned heavily on self-play, it crushed heads-up, but it struggled when facing unpredictable casual players.

Lately I’ve been messing around with some poker bots just out of curiosity. I noticed that some of them play in a super robotic way while others actually feel closer to how real players bluff and adapt. It got me thinking — when it comes to training these AIs, is it better to feed them tons of human gameplay hands or just let them play against themselves endlessly until they “figure it out”? I imagine human data adds realism, but self-play might push strategies way beyond what we normally see at the tables.

I once watched a friend run simulations on his laptop, and the whole thing looked more like a video game marathon than a poker session. He left it running overnight, and by the next morning his fan sounded like it was about to take off. The craziest part is that he said the computer had played millions of hands while he slept. I couldn’t wrap my head around how fast the numbers added up, but it did make me realize how different this tech world is from just sitting at a kitchen table with cards and snacks.