Practice Regret Analysis - 9.9.4 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.9.4 - Regret Analysis

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define regret in the context of Multi-Armed Bandits.

πŸ’‘ Hint: Think about how we measure performance in decision-making.

Question 2

Easy

What is an example of an exploration strategy?

πŸ’‘ Hint: Consider strategies that mix exploration and exploitation.

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 does 'regret' measure in the context of multi-armed bandits?

  • The difference in outcomes
  • The lost revenue
  • The difference between earned rewards and optimal rewards

πŸ’‘ Hint: Think about a scenario where you missed a better choice.

Question 2

True or False: A higher exploration rate always leads to lower regret over time.

  • True
  • False

πŸ’‘ Hint: Consider the initial trade-offs of exploring new actions.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

If after 10 trials, your chosen actions result in a total reward of 50, but the optimal actions could yield 100, what is your cumulative regret?

πŸ’‘ Hint: Divide the total attempts by the maximum possible rewards to find regret.

Question 2

Compare the regret in the Ξ΅-greedy strategy to that of the UCB strategy after 50 rounds. Reflect on how exploration impacts both.

πŸ’‘ Hint: Think about how each strategy learns from previous actions over time.

Challenge and get performance evaluation