An Introduction to Q-Learning Part 2/2
Hugging Face ● Covered by 2 sources
Hugging Face's RL course dropped part 2 on Q-Learning, with a hands-on agent trained on Frozen Lake and a taxi sim. It's the hands-dirty follow-up to the theory in part 1, building toward Deep Q-Learning next.
Based on reporting by Hugging Face — read the original for the full story.
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Hugging Face's Deep Reinforcement Learning course has reached the point where theory turns into code, and that shift matters. Part 2 of Unit 2 walks through Q-Learning, the off-policy, value-based algorithm that underpins a huge chunk of modern RL work, and then has students actually build one from scratch rather than just read about it.
The core idea is a Q-table: a lookup grid where every state-action pair gets a number representing how good that move is. Start with zeros, since the agent knows nothing yet, then update the table as the agent bumps around its environment collecting rewards. The update rule leans on a temporal-difference target, meaning the agent doesn't wait until an episode ends to learn something; it adjusts its estimate after every single step using the immediate reward plus a discounted guess about the best next move.
The course spends real time on epsilon-greedy exploration, the mechanism that decides whether the agent tries something random or sticks with what it currently thinks is best. Epsilon starts near 1.0, so early on the agent is basically flailing, and it decays over training so the agent shifts from exploring to exploiting once its estimates get trustworthy. There's also a clean explainer on why Q-Learning counts as off-policy: the agent acts using the epsilon-greedy policy but updates its table using a strictly greedy policy, a subtle but important distinction from on-policy cousins like Sarsa.
To make this concrete, the course uses a mouse-in-a-maze toy example with a discount rate of 0.99 and a learning rate of 0.1, where cheese is good, poison is instant death worth -10, and running out of five steps just ends things quietly. From there, students move to actual training runs on Frozen Lake and a taxi-navigation environment, using a provided Colab notebook, before checking their understanding with a self-graded quiz. It's not flashy, but it's the kind of unglamorous foundation Hugging Face insists you need before Unit 3, where Deep Q-Learning brings neural networks into the picture.
My take — AI-written commentary, not fact-checked reporting
I like that Hugging Face forces people to hand-code the Q-table before letting neural nets anywhere near it — most of the confusion I see from people jumping straight into Deep Q-Learning traces back to skipping exactly this step. It's a small, unglamorous unit, but it's the kind of open, reproducible teaching material that actually closes the gap between reading papers and understanding them, which is more useful than another flashy leaderboard demo.
Read more about this at: Hugging Face