# 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 are the advantages of policy-gradient over value-based methods? (Check all that apply)

<Question
	choices={[
		{
			text: "Policy-gradient methods can learn a stochastic policy",
			explain: "",
      			correct: true,
		},
		{
			text: "Policy-gradient methods are more effective in high-dimensional action spaces and continuous actions spaces",
			explain: "",
      			correct: true,
		},
    {
			text: "Policy-gradient converges most of the time on a global maximum.",
			explain: "No, frequently, policy-gradient converges on a local maximum instead of a global optimum.",
		},
	]}
/>

### Q2: What is the Policy Gradient Theorem?

<details>
<summary>Solution</summary>

*The Policy Gradient Theorem* is a formula that will help us to reformulate the objective function into a differentiable function that does not involve the differentiation of the state distribution.

<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit6/policy_gradient_theorem.png" alt="Policy Gradient"/>

</details>


### Q3: What's the difference between policy-based methods and policy-gradient methods? (Check all that apply)

<Question
	choices={[
    {
      text: "Policy-based methods are a subset of policy-gradient methods.",
      explain: "",
    },
    {
      text: "Policy-gradient methods are a subset of policy-based methods.",
      explain: "",
      correct: true,
    },
    {
      text: "In Policy-based methods, we can optimize the parameter θ **indirectly** by maximizing the local approximation of the objective function with techniques like hill climbing, simulated annealing, or evolution strategies.",
      explain: "",
      correct: true,
    },
    {
	text: "In Policy-gradient methods, we optimize the parameter θ **directly** by performing the gradient ascent on the performance of the objective function.",
	explain: "",
	correct: true,
	},
	]}
/>


### Q4: Why do we use gradient ascent instead of gradient descent to optimize J(θ)?

<Question
	choices={[
    {
      text: "We want to minimize J(θ) and gradient ascent gives us the direction of the steepest increase of J(θ)",
      explain: "",
    },
		{
			text: "We want to maximize J(θ) and gradient ascent gives us the direction of the steepest increase of J(θ)",
			explain: "",
      correct: true
		},
	]}
/>

Congrats on finishing this Quiz 🥳, if you missed some elements, take time to read the chapter again to reinforce (😏) your knowledge.


<EditOnGithub source="https://github.com/huggingface/deep-rl-class/blob/main/units/en/unit4/quiz.mdx" />