Practice Policy Gradient Methods - 9.6 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.6 - Policy Gradient Methods

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the primary focus of policy gradient methods?

πŸ’‘ Hint: Think about the difference between value and policy.

Question 2

Easy

Explain how A2C combines the roles of actor and critic.

πŸ’‘ Hint: Consider how each component supports the learning process.

Practice 2 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 do policy gradient methods primarily optimize?

  • Value Functions
  • Policies
  • Action Spaces

πŸ’‘ Hint: Think about the word 'policy' in the methods' name.

Question 2

True or False: Value-Based Methods are always superior to Policy-Based Methods.

  • True
  • False

πŸ’‘ Hint: Consider situations with complex action spaces.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design an outlined approach for implementing A2C. What specific challenges would you anticipate while tuning the model?

πŸ’‘ Hint: Think about the interaction between the actor and critic.

Question 2

Compare and contrast PPO and TRPO. When might one be favored over the other?

πŸ’‘ Hint: Consider ease of implementation versus stability constraints.

Challenge and get performance evaluation