Practice ε-greedy - 9.9.3.1 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.9.3.1 - ε-greedy

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does ε stand for in the ε-greedy strategy?

💡 Hint: Think about its role in balancing exploration and exploitation.

Question 2

Easy

In the ε-greedy method, what happens with probability (1 - ε)?

💡 Hint: Consider what the agent does most of the time.

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 ε variable control in the ε-greedy strategy?

  • Exploration only
  • Exploitation only
  • The trade-off between exploration and exploitation

💡 Hint: Think about its impact on making decisions.

Question 2

True or False: High values of ε encourage more exploitation.

  • True
  • False

💡 Hint: Consider what increasing ε would do.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Suppose you have an environment with three arms, where their expected rewards are fluctuating frequently. Design a strategy using ε-greedy to adapt to these changes, specifying how you would choose and adjust ε over time.

💡 Hint: Consider how you can balance adaptability with the need to exploit.

Question 2

You run an experiment using ε-greedy, and results show that while you initially had good explorative results, your performance plateaued over time. Analyze what might have gone wrong and propose corrective measures.

💡 Hint: How might the learning environment's dynamics affect your strategy?

Challenge and get performance evaluation