# Quiz [[quiz]]

The best way to learn and [to avoid the illusion of competence](https://www.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**.

### Q1: What is Reinforcement Learning?

Solution

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**.

### Q2: Define the 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 a ______ to the Agent

### Q3: What's the difference between a state and an observation?

### Q4: A task is an instance of a Reinforcement Learning problem. What are the two types of tasks?

### Q5: What is the exploration/exploitation tradeoff?

Solution

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**.

### Q6: What is a policy?

Solution

- The Policy π **is the brain of our Agent**. It’s the function that tells us what action to take given the state we are in. So it defines the agent’s behavior at a given time.

### Q7: What are value-based methods?

Solution

- 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**.

### Q8: What are policy-based methods?

Solution

- 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**.

Congrats on finishing this Quiz 🥳, if you missed some elements, take time to read again the chapter to reinforce (😏) your knowledge, but **do not worry**: during the course we'll go over again of these concepts, and you'll **reinforce your theoretical knowledge with hands-on**.