Practice Value-based Deep Q-network (dqn) (3.2) - Reinforcement Learning and Decision Making

Practice: Value-Based Deep Q-Network (DQN)

Lesson 3.2Reinforcement Learning and Decision Making

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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 does DQN stand for?
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Think about the combination of Q-Learning and deep learning.
Question 2.
Name one key benefit of using neural networks in DQN.
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Consider how traditional Q-Learning struggles with large inputs.
3Question 3.
What does DQN primarily combine?
- Value-Based Learning and Decision Trees
- Q-Learning and Neural Networks
- Supervised Learning and Unsupervised Learning
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Focus on the components of DQN.
4Question 4.
True or False: Experience replay in DQNs allows the agent to store experiences and use them to stabilize learning.
- True
- False
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Consider how learning might benefit from past information.
Question 5.
Critically analyze the impact of size and quality of experience replay memory in DQNs. How does it affect learning?
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Consider the balance between recent and diverse experiences.
Question 6.
Devise modifications to the DQN architecture to handle a specific application's needs, such as continuous action spaces.
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Think about how DQNs are structured and how they might adapt to different tasks.

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