Practice Exploration Strategies (9.9.3) - Reinforcement Learning and Bandits
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Exploration Strategies

Practice - Exploration Strategies

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

Define exploration in the context of multi-armed bandits.

💡 Hint: Relates to discovering new opportunities.

Question 2 Easy

What does exploitation refer to in reinforcement learning?

💡 Hint: Think about sticking with what you know works.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary goal of exploration in multi-armed bandit problems?

To maximize immediate rewards.
To build a model of possible actions.
To explore new options and gather information.

💡 Hint: Focus on the purpose of trying new things.

Question 2

True or False: Thompson Sampling always selects the action with the highest average reward.

True
False

💡 Hint: Think about what probabilistic choices imply.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a multi-armed bandit algorithm using both ε-greedy and UCB strategies and explain the rationale behind each choice.

💡 Hint: Consider how each strategy contributes to overall learning.

Challenge 2 Hard

Implement a Thompson Sampling algorithm for a simulated ad placement scenario, demonstrating how it adapts over time.

💡 Hint: Focus on how probability distributions drive decision-making.

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