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Machine Learning: Reinforcement Learning Questions
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Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. Generally, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error.

Machine Learning: Reinforcement Learning Questions
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25 Questions

1. Model Predictive Control (MPC)

2. In RL, what is temporal difference?

3. What is a replacement for Exploration & Exploitation?

4. In the total discounted sum of rewards: When γ < 1, what does that mean?

5. What is Reinforcement Learning?

6. What are example problems that can be solved with RL?

7. Reinforcement learning has _______ and ______ labels, aka _______.

8. In RL, an ______ has to learn from ______ in an __________.

9. A countable MDP is defined as a triplet M = (X, A, Po). What is each term?

10. What is a Markov Decision Processes (MDP)?

11. What is hierarchical reinforcement learning?

12. What is a stationary policy?

13. What are core algorithms for RL?

14. This is the formula for RL return. What does it mean?

15. What does off-policy learner mean?

16. In a MDP, what is an episode?

17. What is the emperical advantage of Model predictive control (MPC)?

18. Q-learning

19. What is the difference between RL and supervised ML?

20. In RL, what is π?

21. What is a Markov reward processes (MRP)?

22. What is the relationship between sunk costs and MDP?

23. What is the difference between on-policy and off-policy learner?

24. Alpha–beta pruning

25. Is Q-learning off-policy or on-policy?

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