Building Tic-Tac-Toe AI from Scratch: Exploring Reinforcement Learning and Monte Carlo Methods
research#reinforcement learning📝 Blog|Analyzed: Apr 19, 2026 13:46•
Published: Apr 19, 2026 13:35
•1 min read
•Qiita AIAnalysis
This article offers a fantastic deep-dive into the foundational mechanics of AI by guiding readers through building a Tic-Tac-Toe agent from scratch. It beautifully bridges the gap between traditional static evaluation functions and the revolutionary potential of Reinforcement Learning. By demonstrating how AI can autonomously learn optimal strategies through observation, it provides an incredibly engaging and accessible entry point into advanced machine learning concepts!
Key Takeaways
- •Traditional AI combines static evaluation functions with game tree search, but creating perfect manual evaluations is too complex for advanced games.
- •Reinforcement Learning empowers programs to learn optimal actions dynamically by observing their environment and self-playing.
- •This series marks an exciting transition from hardcoded human logic to autonomous AI learning for game strategy.
Reference / Citation
View Original"Reinforcement Learning is an algorithm in which a program learns how it should act by observing its surrounding environment, and in the case of Tic-Tac-Toe, it learns which move to make by observing the games being played."
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