Practice Markov Decision Processes (mdps) (5.3) - Planning and Decision Making

Practice: Markov Decision Processes (MDPs)

Lesson 5.3Planning and Decision Making

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9 questions

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3 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
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
Define what an MDP is.
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Think about how choices lead to different outcomes.
Question 2.
What does the term 'state' refer to in an MDP?
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Consider what the agent faces at any moment.
3Question 3.
What does MDP stand for?
- Markov Decision Process
- Markov Distribution Process
- Markov Data Process
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Recall the full form of MDP in decision theory.
4Question 4.
True or False: The discount factor γ can only be a value greater than 1.
- True
- False
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Think about how immediate and future rewards are valued.
Question 5.
Given a simple MDP with states S1, S2, and S3, where actions result in probabilities of transitioning between states and rewards are assigned, design a policy that maximizes the expected reward.
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Start by listing out the potential outcomes for each action and their probabilities.
Question 6.
Analyze a real-world scenario in healthcare where MDPs can be applied. Provide a layout of states, actions, and potential rewards.
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Think about how treatments evolve the state of patient health.

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