Tic-Tac-Toe the Hard Way People + AI Research

David and Yannick get started on their project to build competing machine learning systems that play tic-tac-toe. They discuss the human choices that will shape their systems along the way.

David delves into questions around data and training for his model including: What does a tic-tac-toe board “look” like to ML? Plus, an intro to reinforcement learning, the approach Yannick will be taking.

Once we have the data we need—thousands of sample games—how do we turn it into something the ML can train itself on? That means understanding how training works, and what a model is.

David did it! He trained a machine learning model to play tic-tac-toe! How did his model do against a player that makes random tic-tac-toe moves?

Switching gears, we focus on how Yannick’s been training his model using reinforcement learning. He explains the differences from David’s supervised learning approach. We find out how his system performs against a player that makes random tic-tac-toe moves.

David and Yannick’s tic-tac-toe ML agents face-off against each other in tic-tac-toe!