#
Quiz

The best way to learn and to avoid the illusion of competence 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)

Policy-gradient methods can learn a stochastic policy Policy-gradient methods are more effective in high-dimensional action spaces and continuous actions spaces Policy-gradient converges most of the time on a global maximum.

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

Solution

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.

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

Policy-based methods are a subset of policy-gradient methods. Policy-gradient methods are a subset of policy-based methods. 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. In Policy-gradient methods, we optimize the parameter θ **directly** by performing the gradient ascent on the performance of the objective function.

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

We want to minimize J(θ) and gradient ascent gives us the direction of the steepest increase of J(θ) We want to maximize J(θ) and gradient ascent gives us the direction of the steepest increase of J(θ)

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

< > Update on GitHub