Levelling Up My Checkers Game: A Neural Network Upgrade

Introduction

The development of games involving strategic gameplay, such as checkers, offers an interesting challenge, particularly from an AI perspective. Recently, I’ve upgraded my Plotly and Dash checkers game, integrating advanced AI capabilities, improving data handling, and enhancing user interaction. This article dives into the technical and mathematical aspects of these updates, providing a deeper understanding of the game’s underlying mechanics and the intelligence that drives it.

1. Integration of a Neural Network Model

One of the most significant upgrades was the integration of a neural network model to drive AI decisions. This model not only enhances the AI’s ability to make strategic moves but also learns from past games to improve over time.

Neural Network Basics

The neural network used in the game is a type of machine learning model designed to recognize patterns and make decisions based on input data. Here’s how the network is structured:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input as KerasInput

def init_neural_network():
    model = Sequential([
        KerasInput(shape=(64,)),  # Each square on the board…