Practice Learning By Trial And Error (1.1) - Reinforcement Learning and Decision Making

Practice: Learning by trial and error

Lesson 1.1Reinforcement Learning and Decision Making

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9 questions

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4 cards from this section. Quick, good the night before a test.
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6 sample questions

Question 1.
Define an agent in the context of reinforcement learning.
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Think of who is doing the learning.
Question 2.
What role does a reward play in reinforcement learning?
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Consider how we encourage good behavior.
3Question 3.
What is the main objective of an agent in reinforcement learning?
- Minimize errors
- Maximize cumulative reward
- Explore as many actions as possible
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Think about what the agent tries to achieve.
4Question 4.
True or False: Exploration involves using only the already known rewarding actions.
- True
- False
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Consider the active behavior versus the known behavior.
Question 5.
Design an experiment to illustrate the exploration versus exploitation dilemma for agents. Describe the steps and expected outcomes.
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Consider how many times the agent repeats each action and how rewards change over time.
Question 6.
Analyze a scenario where an agent fails to learn effectively from trial and error. What factors might contribute to this difficulty?
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Think about how gradual feedback might affect learning and performance.

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