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Merge pull request #359 from darkrodry/patch-1

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Minor typo in unit 3 introduction
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Thomas Simonini
2023-07-03 15:04:00 +02:00
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@@ -6,7 +6,7 @@
In the last unit, we learned our first reinforcement learning algorithm: Q-Learning, **implemented it from scratch**, and trained it in two environments, FrozenLake-v1 ☃️ and Taxi-v3 🚕.
We got excellent results with this simple algorithm, but these environments were relatively simple because the **state space was discrete and small** (14 different states for FrozenLake-v1 and 500 for Taxi-v3). For comparison, the state space in Atari games can **contain \\(10^{9}\\) to \\(10^{11}\\) states**.
We got excellent results with this simple algorithm, but these environments were relatively simple because the **state space was discrete and small** (16 different states for FrozenLake-v1 and 500 for Taxi-v3). For comparison, the state space in Atari games can **contain \\(10^{9}\\) to \\(10^{11}\\) states**.
But as we'll see, producing and updating a **Q-table can become ineffective in large state space environments.**