Best Practices from Building a Machine Learning Bot for Halite

This post details the development of a machine learning bot for the Halite AI programming challenge. It outlines best practices learned, including choosing appropriate ML approaches (supervised learning over reinforcement learning due to computational constraints), starting with a simplified problem, determining optimal data sample sizes for training, ensuring models evaluate relevant variables to avoid overfitting, and establishing effective performance evaluation metrics by comparing ML bots against each other and baseline logic. It also touches on catching edge cases through controlled testing environments.