Practice Value-based Q-learning (3.1) - Reinforcement Learning and Decision Making

Practice: Value-Based Q-Learning

Lesson 3.1Reinforcement Learning and Decision Making

Practise this section

9 questions

Flashcard drill
3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
Sign up to start the exercise
Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
Sign up to start the quiz

6 sample questions

Question 1.
What does Q-Learning help agents learn?
Show hintHide hint
Think about rewards and actions.
Question 2.
Name one real-world application of Q-Learning.
Show hintHide hint
Consider competitive environments.
3Question 3.
What does the Q in Q-Learning stand for?
- Quality
- Question
- Quantum
Show hintHide hint
Think about what aspect of the actions is being evaluated.
4Question 4.
True or False: Q-Learning is a policy-based algorithm.
- True
- False
Show hintHide hint
Recall the difference between value and policy-based methods.
Question 5.
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.
Show hintHide hint
Use the formula Q(s, a) = Q(s, a) + α[R + γ max_a Q(s', a) − Q(s, a)].
Question 6.
Discuss the implications of using Q-Learning in a real-time environment with continuous actions, such as self-driving cars. What challenges might arise?
Show hintHide hint
Consider how continuous actions may complicate the learning process for agents.

5 more questions in this section. Enroll free to practise them all.