Practice Receives State, Takes Action, Gets Reward (1.3) - Reinforcement Learning and Decision Making

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9 questions

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6 questions · about 4 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does RL stand for?
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Think of the learning process that involves interactions.
Question 2.
What is an agent in RL?
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Consider who is learning in the context of RL.
3Question 3.
What is the main component of the RL process?
- A: State
- B: Action
- C: Reward
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Think about what elements contribute to learning in RL.
4Question 4.
True or False: An agent aims to minimize its cumulative reward.
- True
- False
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Consider the objective of the agent in learning.
Question 5.
Design an RL scenario in a game of Tic-Tac-Toe where an agent learns to play optimally. Describe the states, actions, and possible rewards.
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Think about how the state changes after each move.
Question 6.
Analyze the potential downsides of using exploration-heavy strategies in RL. What could go wrong?
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Consider efficiency and time investment in learning.

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