How to Build an Adaptive Tic-Tac-Toe AI with Reinforcement Learning in JavaScript

This tutorial demonstrates how to create a Tic-Tac-Toe AI that learns winning strategies through Q-learning, where the AI improves by playing games and receiving rewards for good moves. The implementation includes three difficulty levels - beginner (random moves), intermediate (Q-learning with exploration), and expert (minimax algorithm) - with real-time parameter adjustment and visual progress tracking. Players can train the AI through 1,000 rapid self-play games, watch it evolve from random to strategic play, and experiment with learning rates and exploration settings to understand how reinforcement learning adapts through experience.