Practice Multi-armed Bandits (9.9) - Reinforcement Learning and Bandits
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Multi-Armed Bandits

Practice - Multi-Armed Bandits

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

Define the Multi-Armed Bandit problem.

💡 Hint: Think of a gambler faced with several slot machines.

Question 2 Easy

What is the main goal of using exploration strategies in MAB?

💡 Hint: Think about maximizing rewards over time.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary goal of the Multi-Armed Bandit problem?

To explore all options equally
To maximize cumulative rewards
To minimize the number of trials

💡 Hint: Remember the gambling analogy.

Question 2

True or False: Contextual bandits do not use extra information to inform their decisions.

True
False

💡 Hint: Consider what 'context' means.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Consider a scenario where an online platform has to decide which of three ad campaigns to run based on click-through rates. Discuss the implications of using UCB versus Thompson Sampling in this context.

💡 Hint: Think about how each strategy approaches uncertainty and the nature of collected data.

Challenge 2 Hard

Imagine a recommendation system that uses bandit strategies. Design a simple framework for how you would implement this system with emphasis on balancing exploration versus exploitation.

💡 Hint: Consider how user interactions can inform better recommendations over time.

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Reference links

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