Practice Q-learning And Deep Q-networks (10.3) - Reinforcement Learning

Practice: Q-Learning and Deep Q-Networks

Lesson 10.3Reinforcement Learning

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9 questions

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6 sample questions

Question 1.
What is the primary function of Q-Learning?
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Think about its aims regarding rewards.
Question 2.
Define the term 'discount factor' in reinforcement learning.
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Consider how it influences decision-making over time.
3Question 3.
What does Q-Learning aim to maximize?
- Learning rate
- Cumulative reward
- Discount factor
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Focus on the end goal and the results of the updates.
4Question 4.
True or False: DQNs use simple Q-tables to represent the action values.
- True
- False
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Think about the complexity of environments DQNs typically handle.
Question 5.
Given a reinforcement learning scenario, design a Q-Learning algorithm that adapts to both immediate and delayed rewards. How would you choose the learning rate and discount factor?
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Think about how different environments may require different strategies.
Question 6.
Discuss how you would implement experience replay in a DQN for a specific video game. What modifications would be needed compared to basic Q-Learning?
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Consider the implications for training data diversity.

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