Practice Strategies - 9.8.3 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.8.3 - Strategies

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define the exploration-exploitation trade-off.

πŸ’‘ Hint: Think about what it means to try new options versus using what's already known.

Question 2

Easy

What does Ξ΅ in the Ξ΅-greedy strategy represent?

πŸ’‘ Hint: Consider how often the agent tries out new actions with Ξ΅ set.

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 the Ξ΅-greedy strategy allow an agent to do?

  • Exploit always
  • Explore always
  • Balance exploration and exploitation
  • None of the above

πŸ’‘ Hint: Remember what Ξ΅ stands for.

Question 2

True or False: The Softmax strategy guarantees that the best action will always be selected.

  • True
  • False

πŸ’‘ Hint: Consider how probabilities influence outcomes.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Consider an agent using both Ξ΅-greedy and Thompson Sampling strategies in a simulated environment with 5 actions, where the true values are unknown. Design a comparative study and explain the expected outcomes and metrics to observe.

πŸ’‘ Hint: Identify measurable performance indicators to capture the relative strengths of both approaches.

Question 2

Imagine you have a multi-armed bandit problem with several K arms and uncertain rewards. Design a strategy using the Upper Confidence Bound method, stating how you would calculate the exploration bonuses and your decision-making process.

πŸ’‘ Hint: Focus on ensuring that your bonus effectively promotes exploration of less-trialed arms.

Challenge and get performance evaluation