Practice Rewards, Policies, And Value Functions (10.2) - Reinforcement Learning

Practice: Rewards, Policies, and Value Functions

Lesson 10.2Reinforcement Learning

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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.
What is a reward in reinforcement learning?
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Think about what feedback an agent gets.
Question 2.
Explain the difference between deterministic and stochastic policies.
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Consider whether actions are predictable or varied.
3Question 3.
What is the primary goal of an agent in reinforcement learning?
- To minimize risk
- To maximize total expected reward
- To act randomly
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Think about what agents are trying to achieve.
4Question 4.
True or False: A deterministic policy always leads to the same action from a given state.
- True
- False
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Consider if actions change or stay the same for states.
Question 5.
How would an agent's learning change if rewards were delivered only after a series of actions instead of immediately?
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Consider the impact on feedback timing in learning.
Question 6.
Design a simple scenario using a stochastic policy and illustrate how it allows for exploration compared to a deterministic one.
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Think about how randomness affects decision-making.

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