Practice Estimating Value Functions from episodes - 9.4.2 | 9. Reinforcement Learning and Bandits | Advance Machine Learning
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9.4.2 - Estimating Value Functions from episodes

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define what a value function is in reinforcement learning.

πŸ’‘ Hint: What does the agent aim to maximize?

Question 2

Easy

What is an episode?

πŸ’‘ Hint: Think of it as a complete journey in an environment.

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 is a value function in reinforcement learning?

πŸ’‘ Hint: Think about what the agent wants to achieve.

Question 2

In First-Visit Monte Carlo, how is the value of a state determined?

  • A) By all visits to the state
  • B) By the last visit to the state
  • C) By the first visit to the state

πŸ’‘ Hint: Focus on when the estimate is captured.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You are given episodes from a simple grid world. Calculate the estimated value of specific states using both First-Visit and Every-Visit Monte Carlo methods.

πŸ’‘ Hint: Use the rewards from episodes fitting the definitions of each Monte Carlo method.

Question 2

Evaluate a scenario where the reward structure changes over time. How would First-Visit Monte Carlo differ from Every-Visit Monte Carlo in this context?

πŸ’‘ Hint: Consider how the timing of reward changes affects learning.

Challenge and get performance evaluation