Practice Exploration Strategies - 9.9.3 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.9.3 - Exploration Strategies

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define exploration in the context of multi-armed bandits.

πŸ’‘ Hint: Relates to discovering new opportunities.

Question 2

Easy

What does exploitation refer to in reinforcement learning?

πŸ’‘ Hint: Think about sticking with what you know works.

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 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.

πŸ’‘ 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

πŸ’‘ Hint: Think about what probabilistic choices imply.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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

πŸ’‘ Hint: Consider how each strategy contributes to overall learning.

Question 2

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

πŸ’‘ Hint: Focus on how probability distributions drive decision-making.

Challenge and get performance evaluation