Building a Poker Bot with AI: Python, GPT, and Computer Vision

This article explores the journey of creating a poker bot using AI technologies. The initial idea was to develop a program that could play poker autonomously and profitably, leveraging AI's ability to make rational decisions without emotional biases. The project involved integrating ChatGPT with Python libraries to analyze the game and make strategic choices. The author shares their experience in developing this project, highlighting the use of AI, Python, and Computer Vision (CV) to transform a solo developer into a mini-R&D team.

The project was divided into four main tasks: implementing Monte Carlo simulations for decision-making, developing Computer Vision to reliably extract data from the screen, creating a user-friendly GUI for continuous analysis, and building an autobot to automate actions. The first two tasks have been completed, while the latter two are still under consideration due to ethical and technical challenges. The author details the four-week process from initial concept to a working alpha version, emphasizing the roles of ChatGPT, Cursor, and Roboflow.

Poker is a game of strategy where players aim to create the best hand or force opponents to fold. Texas Hold'em is the most popular variant, involving closed cards for players and shared community cards. The game progresses through stages like pre-flop, flop, turn, and river. Winning depends on having the best combination or successfully bluffing. Understanding concepts like positions, equity, and Expected Value (EV) is crucial for strategic play. EV represents the average profit or loss from a specific action, guiding decisions on whether to call, raise, or fold.

Mathematics is essential for calculating EV, which determines the profitability of actions. The formula involves considering the stake, the current pot, and the equity (probability of winning). Equity is calculated using the Monte Carlo method, which simulates numerous random outcomes to estimate probabilities. This method is particularly useful when analytical calculations are too complex. The author provides a Python code example using the 'treys' library to calculate equity, simplifying the mathematical complexity.

Computer Vision (CV) enables computers to interpret images and videos, identifying objects and their coordinates. The author chose YOLO models for object detection in the poker bot project. YOLO models are capable of various tasks, including classification, detection, segmentation, and pose estimation. For analyzing the poker table and detecting cards, detection models are suitable. The author selected the yolo11n.pt model for its balance of speed and accuracy.

YOLO (You Only Look Once) is a family of real-time object detection models. Different versions of YOLO models are available, each with varying parameters and computational requirements. The author chose yolo11n.pt due to its lower computational demands. Parameters indicate the model's capacity to learn complex dependencies, while GFLOPs affect inference speed. The article details the process of training the YOLO model to detect poker cards, which involves preparing a dataset and fine-tuning the model.

Roboflow is a tool that simplifies the process of preparing data for training computer vision models. It allows users to upload and annotate images, apply augmentations to increase data volume, and manage datasets efficiently. The author used Roboflow to annotate and augment images of poker cards and tables. The platform offers a free plan suitable for small projects, with limitations on the number of projects and original images.

Training a YOLO model involves feeding it a dataset to learn patterns. The dataset is typically divided into training, validation, and test sets. Augmentation techniques are used to expand the dataset by creating modified versions of existing images. The author explains the difference between training, fine-tuning, and overfitting. Fine-tuning involves training an already trained model on a new dataset. The article provides commands for training the YOLO model using Python and the Ultralytics library, including parameters for epochs, image size, and batch size.

The development of a poker bot demonstrates how AI technologies can empower solo developers. By leveraging tools like ChatGPT, Cursor, YOLO, and Roboflow, individuals can tackle complex projects that would traditionally require a team. The author's journey highlights the potential of AI to democratize technology and enable innovation at the individual level. The project showcases the transformative impact of AI, Python, and Computer Vision in modern software development.