ComputerPoker.ai

We trained PyTorch models on solved poker scenarios for post-flop, turn, and river situations. The planned "killer feature" is to give users feedback on their poker play in the flow of a simulated poker tournament or simulated cash game scenario. The goal is to play against "GTO Bots" (Game Theory Optimal Bots) to learn how to play closer to GTO.^1

Poker has been a passion of mine for a few years now, I find the game incredibly intellectually stimulating as well as a tremendous catalyst for personal growth, and this project has been a great way to channel that energy.

The web app uses Django/Channels/WebSockets. We've built an internal discounted CFR solver as well, hopefully building up to multiway scenarios in the future. The webapp is still in Beta/gated, and you're interested in learning more please email contact at surlesol dot com.

We are thinking of pricing $8/month or $74.99/year, with the rationale that this will be far less expensive than learning by experience at even micro stakes for online poker, with better feedback for learning, and at least we make it explicit that you're competing against bots ;-)

1. I am aware that GTO play is not always optimal, especially in live poker where live tells are available, and often exploitative strategies fare better than pure GTO. The target audience for ComputerPoker.ai is not hardcore poker pros, there's plenty of existing software for that, but rather those individuals looking to get acquainted with what GTO play "feels like." Then, with this knowledge in hand, knowing what the GTO play would be given various assumptions about our range and a reasonable opponent's range, we can deviate from the GTO play as deemed necessary.