Practice Ε-greedy (9.9.3.1) - Reinforcement Learning and Bandits - Advance Machine Learning
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ε-greedy

Practice - ε-greedy - 9.9.3.1

Learning

Practice Questions

Test your understanding with targeted questions

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.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

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.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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.

Challenge 2 Hard

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?

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Reference links

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