Practice Solving Mdps (5.3.3) - Planning and Decision Making - AI Course Fundamental

Practice: Solving MDPs

Lesson 5.3.3Planning and Decision Making

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11 questions

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5 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
5 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does MDP stand for?
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Think of the process of making decisions under uncertainty.
Question 2.
Identify a method used to solve MDPs.
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One focuses on immediate rewards, the other on iterative policies.
3Question 3.
What is the main purpose of a Markov Decision Process?
- To model deterministic processes
- To model decision-making under uncertainty
- To simplify decision-making
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Think about environments where not everything is predictable.
4Question 4.
True or False: Value iteration directly optimizes a policy.
- True
- False
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Consider the difference between values and policies.
Question 5.
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.
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Remember to consider the physical layout and obstacles within your design.
Question 6.
Using policy iteration, discuss how you would tackle the problem of resource allocation in a hospital. Identify the states, actions, policies, and expected rewards.
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Focus on the specific needs of patients and how they influence resource distribution.

7 more questions in this section. Enroll free to practise them all.