From 21717b3c09b6aef14bb665d43e355cc16e8a417d Mon Sep 17 00:00:00 2001
From: Thomas Simonini <simonini.thomas.pro@gmail.com>
Date: Wed, 1 Jun 2022 19:15:25 +0200
Subject: [PATCH] Create quiz2.md

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+# Knowledge Check ✔️
+
+The best way to learn and [avoid the illusion of competence](https://fr.coursera.org/lecture/learning-how-to-learn/illusions-of-competence-BuFzf) **is to test yourself.** This will help you to find **where you need to reinforce your knowledge**. 
+
+📝 Take a piece of paper and try to answer by writing, **then check the solutions**.
+
+### Q1: What is Q-Learning?
+
+<details>
+<summary>Solution</summary>
+
+Q-Learning is **the algorithm we use to train our Q-Function**, an action-value function that determines the value of being at a particular state and taking a specific action at that state.
+
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-q-part2#what-is-q-learning
+</details>
+
+### Q2: What is a Q-Table?
+
+<details>
+<summary>Solution</summary>
+
+ Q-table is the "internal memory" of our agent where each cell corresponds to a state-action value pair value. Think of this Q-table as the memory or cheat sheet of our Q-function.
+  
+
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-q-part2#what-is-q-learning
+</details>
+
+### Q3: Why if we have an optimal Q-function Q* we have an optimal policy?
+
+<details>
+<summary>Solution</summary>
+  
+Because if we have an optimal Q-function, we have an optimal policy since we know for each state what is the best action to take.
+  
+<img src="https://huggingface.co/blog/assets/73_deep_rl_q_part2/link-value-policy.jpg" alt="link value policy"/>
+
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-q-part2#what-is-q-learning
+</details>
+
+### Q4: Can you explain what is Epsilon-Greedy Strategy?
+
+<details>
+<summary>Solution</summary>
+Epsilon Greedy Strategy is a **policy that handles the exploration/exploitation trade-off**.
+
+The idea is that we define epsilon ɛ = 1.0:
+
+- With *probability 1 — ɛ* : we do exploitation (aka our agent selects the action with the highest state-action pair value).
+- With *probability ɛ* : we do exploration (trying random action).
+  
+<img src="https://huggingface.co/blog/assets/73_deep_rl_q_part2/Q-learning-4.jpg" alt="Epsilon Greedy"/>
+  
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-q-part2#the-q-learning-algorithm
+  
+</details>
+
+### Q5: How do we update the Q value of a state, action pair?
+<img src="assets/img/q-update-ex.jpg" alt="Q Update exercise"/>
+
+<details>
+<summary>Solution</summary>
+  <img src="assets/img/q-update-solution.jpg" alt="Q Update exercise"/>
+  📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-q-part2#the-q-learning-algorithm
+</details>
+
+### Q6: What's the difference between on-policy and off-policy
+
+<details>
+<summary>Solution</summary>
+  <img src="https://huggingface.co/blog/assets/73_deep_rl_q_part2/off-on-4.jpg" alt="On/off policy"/>
+  📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-q-part2#off-policy-vs-on-policy
+</details>
+
+
+---
+
+Congrats on **finishing this Quiz** 🥳, if you missed some elements, take time to [read the chapter again](https://huggingface.co/blog/deep-rl-q-part2) to reinforce (😏) your knowledge.
+
+**Keep Learning, Stay Awesome**
+
+