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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 are the two main approaches to find optimal policy?
+
+<details>
+<summary>Solution</summary>
+  
+The two main approaches are:
+- *Policy-based methods*: **Train the policy directly** to learn which action to take given a state.
+- *Value-based methods* : Train a value function to **learn which state is more valuable and use this value function to take the action that leads to it**.
+  
+<img src="assets/img/two-approaches.jpg" alt="Two approaches of Deep RL"/>
+  
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-q-part1#what-is-rl-a-short-recap
+  
+</details>
+
+
+### Q2: What is the Bellman Equation?
+
+<details>
+<summary>Solution</summary>
+  
+**The Bellman equation is a recursive equation** that works like this: instead of starting for each state from the beginning and calculating the return, we can consider the value of any state as:
+
+$R_{t+1} + ( gamma * V(S_{t+1}))$
+
+The immediate reward + the discounted value of the state that follows
+
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-q-part1#the-bellman-equation-simplify-our-value-estimation
+  
+</details>
+
+
+### Q3: Define each part of the Bellman Equation
+
+<img src="assets/img/bellman4-quiz.jpg" alt="Bellman equation quiz"/>
+
+
+<details>
+<summary>Solution</summary>
+
+<img src="assets/img/bellman4.jpg" alt="Bellman equation solution"/>
+  
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-q-part1#the-bellman-equation-simplify-our-value-estimation
+  
+</details>
+
+### Q4: What is the difference between Monte Carlo and Temporal Difference learning methods?
+
+<details>
+<summary>Solution</summary>
+  
+There are two types of methods to learn a policy or a value function:
+- With the *Monte Carlo method*, we update the value function **from a complete episode**, and so we use the actual accurate discounted return of this episode.
+- With the *TD Learning method*, we update the value function **from a step, so we replace Gt that we don't have with an estimated return called TD target**.
+
+<img src="assets/img/summary-learning-mtds.jpg" alt="summary-learning-mtds"/>
+
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-q-part1#monte-carlo-vs-temporal-difference-learning
+
+</details>