Practice Stochastic Bandits - 9.9.2.1 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.9.2.1 - Stochastic Bandits

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is a stochastic bandit?

πŸ’‘ Hint: Think about how you would make choices with uncertain outcomes.

Question 2

Easy

Define exploration in stochastic bandits.

πŸ’‘ Hint: What would you do to gather more information about rewards?

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What defines a stochastic bandit?

  • A model with known rewards
  • A model with fixed unknown distributions
  • A model with continuous feedback

πŸ’‘ Hint: Focus on what 'stochastic' implies about the rewards.

Question 2

True or False: Exploration is less important than exploitation.

  • True
  • False

πŸ’‘ Hint: Think about why learning is necessary in uncertain environments.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Consider a scenario where you have three advertising options with unknown click-through rates. Explain how you would apply the Ξ΅-greedy strategy over ten trials.

πŸ’‘ Hint: Think about how often you'd switch versus stick with the current best performer.

Question 2

Create a simulation for a stochastic bandit with five arms where the true rewards for each arm are hidden. Explain how you would implement Thompson Sampling and what outcomes to track.

πŸ’‘ Hint: Consider how you would keep track of successes and how it affects future probabilities.

Challenge and get performance evaluation