Inspired by AlphaGo’s Move 37 — learn how agents explore, exploit, and win
Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python
In 2016, Go world champion Lee Sedol faced an opponent made not of flesh and blood — but of lines of code.
It soon became clear: The human would lose.
In the end, Lee Sedol lost 4:1.
That was the moment the world realized: This AI doesn’t play like a human — it plays better.
But what does that kind of learning actually look like?
Last week, I rewatched the AlphaGo documentary — and found it just as fascinating as the first time.
Like many others, I often don’t have the time to dive deep into topics like reinforcement learning.
So I set out to build a project that’s simple enough to start — and real enough to matter.
When you’re working a regular 8-to-5 job, the hardest part isn’t curiosity — it’s time.
Time to immerse yourself in a new topic.
Time to finally dig into something you’ve been meaning to explore.
And when you do find that time?
You need the right entry point.
Something practical.
Something you can tackle in an afternoon or evening.
At least, that’s how it is for me.
In this article, I walk you through a step-by-step project to build a simple Q-learning agent using Tic Tac Toe.
Grab your ☕ and the code and let’s dive in!
👉 Full article Reinforcement Learning Made Simple: Build a Q-Learning Agent in Python
👉 You can find the code in this GitHub repository.
👉 Great introduction book about RL: Reinforcement Learning: An Introduction by Richard S. Sutton & Andre G. Barto
☕ P.S. Yesterday, the 100th person signed up for Data Science Espresso. Whether you’ve been here from the start or just joined this week: I just wanted to say a big thank you.
Have a great Sunday, Sarah💕
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