Practice Key Rl Algorithms (3) - Reinforcement Learning and Decision Making

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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 is Q-Learning?
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It's a value-based method.
Question 2.
What is the purpose of DQNs?
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Think of environments with many possible states.
3Question 3.
What does Q-Learning focus on?
- Maximizing exploration
- Learning value of actions
- Directly learning policies
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It's a foundational value-based technique.
4Question 4.
True or False: DQNs can handle large state spaces efficiently.
- True
- False
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Think about how the complexity of states is managed.
Question 5.
Design a simple agent using Q-Learning for a grid-based game where the goal is to reach the top-right corner. Outline the Q-table structure and the expected updates.
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Consider how rewards would be assigned in this environment.
Question 6.
Compare the effectiveness of DQNs versus classical Q-Learning in a scenario involving high-dimensional sensor data, such as in robotics.
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Think about the capabilities of neural networks in processing complex data.

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