Practice Deep Reinforcement Learning (drl) (4) - Reinforcement Learning and Decision Making

Practice: Deep Reinforcement Learning (DRL)

Lesson 4Reinforcement 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 DRL stand for?
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Think about what combines deep learning with reinforcement learning.
Question 2.
What is the purpose of experience replay?
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It allows agents to learn from earlier actions.
3Question 3.
What is the main advantage of DRL over traditional RL?
- It uses simpler algorithms.
- It requires less computation power.
- It handles high-dimensional state spaces effectively.
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Think about the types of problems DRL is used to solve.
4Question 4.
True or False: Experience replay improves the efficiency of learning in agents.
- True
- False
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Consider how past experiences can be beneficial in the learning process.
Question 5.
Design a simple DRL algorithm for a game where an agent must avoid obstacles and collect coins. Explain the choice of architecture and the role of DRL components like experience replay and target networks.
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Think about how each component contributes to learning effectively in complex environments.
Question 6.
Investigate the balance between exploration and exploitation in DRL. Provide an in-depth analysis of how it affects long-term learning outcomes.
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Consider real-world examples where this balance is critical for successful outcomes.

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