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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. What is a Markov reward processes (MRP)?

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

3. What is Reinforcement Learning?

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

5. In a MDP, what is an episode?

6. Model Predictive Control (MPC)

7. In RL, what is temporal difference?

8. What is hierarchical reinforcement learning?

9. What is a stationary policy?

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

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

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

13. Q-learning

14. Alpha–beta pruning

15. What does off-policy learner mean?

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

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

18. What are core algorithms for RL?

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

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

21. What is a replacement for Exploration & Exploitation?

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

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

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

25. In RL, what is π?

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