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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does Q-Learning help agents learn?
💡 Hint: Think about rewards and actions.
Question 2
Easy
Name one real-world application of Q-Learning.
💡 Hint: Consider competitive environments.
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 does the Q in Q-Learning stand for?
- Quality
- Question
- Quantum
💡 Hint: Think about what aspect of the actions is being evaluated.
Question 2
True or False: Q-Learning is a policy-based algorithm.
- True
- False
💡 Hint: Recall the difference between value and policy-based methods.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have an environment with three states and two possible actions at each state. Create a Q-table demonstrating how to update values after receiving certain rewards for actions taken.
💡 Hint: Use the formula Q(s, a) = Q(s, a) + α[R + γ max_a Q(s', a) − Q(s, a)].
Question 2
Discuss the implications of using Q-Learning in a real-time environment with continuous actions, such as self-driving cars. What challenges might arise?
💡 Hint: Consider how continuous actions may complicate the learning process for agents.
Challenge and get performance evaluation