Practice Challenges: Stability, Exploration, Sample Efficiency - 9.7.6 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.7.6 - Challenges: Stability, Exploration, Sample Efficiency

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define stability in the context of deep reinforcement learning.

πŸ’‘ Hint: Think about how training can sometimes go wrong.

Question 2

Easy

What is exploration?

πŸ’‘ Hint: Consider how you might try new things in a game.

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 stability refer to in deep reinforcement learning?

  • Consistency in learning
  • Speed of training
  • Quality of rewards

πŸ’‘ Hint: Think about how an algorithm should behave during training.

Question 2

True or False: Exploitation involves testing out new strategies.

  • True
  • False

πŸ’‘ Hint: Remember the definitions of exploration vs. exploitation.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Analyze a real-world scenario where low sample efficiency affects learning. How would you improve sample efficiency in that context?

πŸ’‘ Hint: Think about opportunities to leverage simulations.

Question 2

Propose a solution to stabilize training in a deep RL system that's experiencing oscillations. What adjustments can be made?

πŸ’‘ Hint: Consider methods that create a buffer between current and target learning.

Challenge and get performance evaluation