3.1 - Value-Based Q-Learning
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What does Q-Learning help agents learn?
💡 Hint: Think about rewards and actions.
Name one real-world application of Q-Learning.
💡 Hint: Consider competitive environments.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does the Q in Q-Learning stand for?
💡 Hint: Think about what aspect of the actions is being evaluated.
True or False: Q-Learning is a policy-based algorithm.
💡 Hint: Recall the difference between value and policy-based methods.
1 more question available
Challenge Problems
Push your limits with advanced challenges
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)].
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.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.