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+# Quiz Time 🥳
+
+The best way to learn and [to 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 Reinforcement Learning?
+
+<details>
+<summary>Solution</summary>
+  
+Reinforcement learning is a **framework for solving control tasks (also called decision problems)** by building agents that learn from the environment by interacting with it through trial and error and **receiving rewards (positive or negative) as unique feedback**.
+
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-intro#a-formal-definition
+  
+</details>
+
+### Q2: Define the RL Loop
+
+<img src="assets/img/rl-loop-ex.jpg" alt="Exercise RL Loop"/>
+
+At every step:
+- Our Agent receives ______ from the environment
+- Based on that ______ the Agent takes an ______
+- Our Agent will move to the right
+- The Environment goes to a ______
+- The Environment gives ______ to the Agent
+
+<details>
+<summary>Solution</summary>
+
+<img src="assets/img/rl-loop-solution" alt="Exercise RL Solution"/>
+  
+
+At every step:
+- Our Agent receives **state s0** from the environment
+- Based on that **state s0** the Agent takes an **action a0**
+- Our Agent will move to the right
+- The Environment goes to a **new state s1**
+- The Environment gives **a reward r1** to the Agent
+  
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-intro#the-rl-process
+
+</details>
+
+
+### Q3: What's the difference between a state and an observation?
+
+<details>
+<summary>Solution</summary>
+  
+- *The state* is a **complete description of the state of the world** (there is no hidden information), in a fully observed environment. For instance, in chess game, we receive a state from the environment since we have access to the whole checkboard information.
+  
+- *The observation* is a **partial description of the state**. In a partially observed environment. For instance, in Super Mario Bros, we only see a part of the level close to the player, so we receive an observation.
+
+<img src="assets/img/obs_space_recap.jpg" alt="Observation Space Recap"/>  
+
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-intro#observationsstates-space
+
+</details>
+
+### Q4: A task is an instance of a Reinforcement Learning problem. What are the two types of tasks?
+<details>
+<summary>Solution</summary>
+  
+- *Episodic task* : we have a **starting point and an ending point (a terminal state)**. This creates an episode: a list of States, Actions, Rewards, and new States. For instance, think about Super Mario Bros: an episode begin at the launch of a new Mario Level and ending when you’re killed or you reached the end of the level.
+  
+- *Continous task* : these are tasks that **continue forever (no terminal state)**. In this case, the agent must learn how to choose the best actions and simultaneously interact with the environment.
+  
+<img src="assets/img/tasks.jpg" alt="Task"/>  
+  
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-intro#type-of-tasks
+
+</details>
+
+### Q5: What is the exploration/exploitation tradeoff?
+
+<details>
+<summary>Solution</summary>
+  
+In Reinforcement Learning, we need to **balance how much we explore the environment and how much we exploit what we know about the environment**. 
+
+- *Exploration* is exploring the environment by **trying random actions in order to find more information about the environment**.
+
+- *Exploitation* is **exploiting known information to maximize the reward**.
+  
+<img src="assets/img/expexpltradeoff.jpg" alt="Exploration/exploitation tradeoff"/>  
+  
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-intro#exploration-exploitation-tradeoff
+</details>
+
+### Q6: What is a policy?
+
+<details>
+<summary>Solution</summary>
+  
+- The Policy π **is the brain of our Agent**, it’s the function that tell us what action to take given the state we are. So it defines the agent’s behavior at a given time.
+  
+<img src="assets/img/policy.jpg" alt="Policy"/>  
+
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-intro#the-policy-%CF%80-the-agents-brain
+</details>
+
+
+### Q7: What are value-based methods?
+
+<details>
+<summary>Solution</summary>
+  
+- Value-based methods is one of the main approaches for solving RL problems.
+- In Value-based methods, instead of training a policy function, **we train a value function that maps a state to the expected value of being at that state**.
+
+<img src="assets/img/value.jpg" alt="Value illustration"/>
+
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-intro#value-based-methods
+</details>
+
+### Q8: What are policy-based methods?
+
+<details>
+<summary>Solution</summary>
+  
+- In *Policy-Based Methods*, we learn a **policy function directly**.
+- This policy function will **map from each state to the best corresponding action at that state**. Or a **probability distribution over the set of possible actions at that state**.
+
+<img src="assets/img/policy.jpg" alt="Policy illustration"/>
+  
+📖 If you don't remember, check 👉 https://huggingface.co/blog/deep-rl-intro#value-based-methods
+  
+
+</details>
+
+---
+
+Congrats on **finishing this Quiz** 🥳, if you missed some elements, take time to [read again the chapter](https://huggingface.co/blog/deep-rl-intro) to reinforce (😏) your knowledge.
+
+**Keep Learning, Stay Awesome**