Practice Strategies (9.8.3) - Reinforcement Learning and Bandits - Advance Machine Learning
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Practice Questions

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Question 1 Easy

Define the exploration-exploitation trade-off.

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

Question 2 Easy

What does ε in the ε-greedy strategy represent?

💡 Hint: Consider how often the agent tries out new actions with ε set.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does the ε-greedy strategy allow an agent to do?

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

💡 Hint: Remember what ε stands for.

Question 2

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

True
False

💡 Hint: Consider how probabilities influence outcomes.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Consider an agent using both ε-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.

💡 Hint: Identify measurable performance indicators to capture the relative strengths of both approaches.

Challenge 2 Hard

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.

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

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