Practice Solving MDPs - 5.3.3 | Planning and Decision Making | AI Course Fundamental
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5.3.3 - Solving MDPs

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does MDP stand for?

💡 Hint: Think of the process of making decisions under uncertainty.

Question 2

Easy

Identify a method used to solve MDPs.

💡 Hint: One focuses on immediate rewards, the other on iterative policies.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the main purpose of a Markov Decision Process?

  • To model deterministic processes
  • To model decision-making under uncertainty
  • To simplify decision-making

💡 Hint: Think about environments where not everything is predictable.

Question 2

True or False: Value iteration directly optimizes a policy.

  • True
  • False

💡 Hint: Consider the difference between values and policies.

Solve 3 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design an MDP for a delivery robot navigating an office building with obstacles. Outline the states, actions, rewards, and how you would implement value iteration for this scenario.

💡 Hint: Remember to consider the physical layout and obstacles within your design.

Question 2

Using policy iteration, discuss how you would tackle the problem of resource allocation in a hospital. Identify the states, actions, policies, and expected rewards.

💡 Hint: Focus on the specific needs of patients and how they influence resource distribution.

Challenge and get performance evaluation