Practice Q-learning (10.3.1) - Reinforcement Learning - AI Course Fundamental

Practice: Q-Learning

Lesson 10.3.1Reinforcement Learning

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9 questions

Flashcard drill
4 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 does Q in Q-Learning stand for?
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Think about what the action-value function does in the context of learning.
Question 2.
Define trial and error learning.
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Consider how we learn new skills.
3Question 3.
What is the main function of Q-Learning?
- To create a model of the environment
- To learn the optimal action-value function
- To minimize errors
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Think about the ultimate goal of agents in reinforcement learning.
4Question 4.
True or False: Q-Learning requires knowledge of the environment's rules.
- True
- False
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Focus on what 'model-free' means.
Question 5.
Suppose an agent applies Q-Learning in a grid world where it can move in four directions. How would it update its Q-values when it receives a reward for moving toward the goal?
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Think about the components of the update rule.
Question 6.
Describe a scenario where using a very high learning rate ( $\alpha$ ) might adversely affect an agent's learning in Q-Learning.
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Consider the balance between stability and adaptability in learning.

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