Practice Reinforcement Learning (2.3) - Introduction to Machine Learning

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8 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
2 questions · about 1 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
Define what an agent is in reinforcement learning.
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Think of a character in a video game.
Question 2.
What feedback does an agent receive in RL?
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Consider how players earn points or lose lives.
3Question 3.
What does an agent do in reinforcement learning?
- Analyzes data in a dataset.
- Takes actions to maximize rewards.
- Classifies different classes.
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Think about how game characters score points.
4Question 4.
True or False: Reinforcement learning uses labeled datasets.
- True
- False
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Contrast it with supervised learning.
Question 5.
Design a reinforcement learning scenario to train a robot to navigate a maze, detailing its states, actions, and potential rewards.
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Consider the feedback loop of actions and learning from success or failure.
Question 6.
Discuss how reinforcement learning can improve a game-playing AI. What metrics would you use to evaluate its performance?
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Think about how AIs adapt and compete against human players.

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