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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does Q in Q-Learning stand for?
💡 Hint: Think about what the action-value function does in the context of learning.
Question 2
Easy
Define trial and error learning.
💡 Hint: Consider how we learn new skills.
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 the main function of Q-Learning?
- To create a model of the environment
- To learn the optimal action-value function
- To minimize errors
💡 Hint: Think about the ultimate goal of agents in reinforcement learning.
Question 2
True or False: Q-Learning requires knowledge of the environment's rules.
- True
- False
💡 Hint: Focus on what 'model-free' means.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Suppose an agent applies Q-Learning in a grid world where it can move in four directions. How would it update its Q-values when it receives a reward for moving toward the goal?
💡 Hint: Think about the components of the update rule.
Question 2
Describe a scenario where using a very high learning rate ($\alpha$) might adversely affect an agent's learning in Q-Learning.
💡 Hint: Consider the balance between stability and adaptability in learning.
Challenge and get performance evaluation