Practice Estimating Value Functions From Episodes (9.4.2) - Reinforcement Learning and Bandits
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Estimating Value Functions from episodes

Practice - Estimating Value Functions from episodes

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

Define what a value function is in reinforcement learning.

💡 Hint: What does the agent aim to maximize?

Question 2 Easy

What is an episode?

💡 Hint: Think of it as a complete journey in an environment.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is a value function in reinforcement learning?

💡 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

💡 Hint: Focus on when the estimate is captured.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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.

💡 Hint: Use the rewards from episodes fitting the definitions of each Monte Carlo method.

Challenge 2 Hard

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?

💡 Hint: Consider how the timing of reward changes affects learning.

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Reference links

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