Holdem basic core structures from scratch.

This commit is contained in:
Jan-Peter von Hunnius 2025-10-10 19:48:24 +02:00
parent f02c35bef3
commit e94bf42dce
53 changed files with 6316 additions and 11906 deletions

46
docs/VISION.md Normal file
View file

@ -0,0 +1,46 @@
# Poker AI Testbed & Live Server Vision
## Overall Goal
Create a **comprehensive poker AI ecosystem** with two main components:
- **Massive Simulation Testbed** – For developing and testing bot strategies across all game types (NL/FL/PL, Cash/SNG/MTT) with configurable parameters
- **Live Action Server** – For deploying bots in real poker environments with automatic table selection
---
## Core Architecture Components
### 1. Simulation Testbed
- **Configurable bots** (Rule-based, Nash EQ, Statistical, Hand-reading, Reinforcement Learning)
- **Mass parallel simulations** with GPU acceleration (e.g., for turn and river rollouts)
- **TOML-driven configuration** for all parameters of bots and simulations
- **Comprehensive result analysis** and bot evolution
### 2. Live Action Server
- **Generic protocol** for client communication
- **Early folding capability** (bots can fold without being polled)
- **Multiple client adapters** for different poker sites
- **Automated table selection** based on game configuration
- **Real-time strategy adaptation**
### 3. Bot Concepts
- **Modular Strategy** per stage and game situation – e.g., low stack in a tournament, preflop vs. postflop.
Plugable sub-strategies and decision methods (first applicable, random, most aggressive, least aggressive, etc.)
- **Generic helper functions** for strategies like ICM, ppot, not, hand strength, against one and multiple opponents
- **Incremental, asynchronous rollout** of turn and river on flop and river on turn
- **Flop Playability score** for preflop based on precalculated rollouts for all possible flops and starting hands
- **Opponent hand prediction and statistics**, for instance in Poker Tracker format
- **Real-time strategy adaptation**, e.g., reinforcement learning
---
## Expected Outcomes
- **Research Platform:** Systematic testing of poker AI strategies
- **Live Deployment:** Bots that can play on real poker sites
- **Strategy Insights:** Understanding what works in different game conditions
- **Bot Evolution:** Self-improving systems through simulation and adaptation