Practice Policy, Value Function, Q-value (9.2.4) - Reinforcement Learning and Bandits
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Policy, Value Function, Q-Value

Practice - Policy, Value Function, Q-Value

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is a policy in reinforcement learning?

💡 Hint: Think about how a plan guides actions.

Question 2 Easy

Define Q-value in simple terms.

💡 Hint: It includes potential future rewards.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does a policy in reinforcement learning do?

A guide for creating rewards
Defines the action to take in a state
Measures the effectiveness of actions

💡 Hint: Remember, think about the agent's decisions.

Question 2

True or False: The Q-value only considers immediate rewards.

True
False

💡 Hint: Consider how future actions influence current decisions.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a policy for a self-driving car that has to navigate through traffic lights and pedestrians. Discuss how value functions and Q-values inform decisions at each stage.

💡 Hint: Consider how different factors affect decisions in a dynamic environment.

Challenge 2 Hard

Create a table comparing the benefits and limitations of using a value function versus a Q-value approach in reinforcement learning scenarios. Provide an example of when one might be more effective than the other.

💡 Hint: Reflect on scenario complexities while creating your comparison.

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Reference links

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