Practice Regret Analysis (9.9.4) - Reinforcement Learning and Bandits - Advance Machine Learning
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Regret Analysis

Practice - Regret Analysis

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

Define regret in the context of Multi-Armed Bandits.

💡 Hint: Think about how we measure performance in decision-making.

Question 2 Easy

What is an example of an exploration strategy?

💡 Hint: Consider strategies that mix exploration and exploitation.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

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

💡 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

💡 Hint: Consider the initial trade-offs of exploring new actions.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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?

💡 Hint: Divide the total attempts by the maximum possible rewards to find regret.

Challenge 2 Hard

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

💡 Hint: Think about how each strategy learns from previous actions over time.

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Reference links

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