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Reinforcement Learning Practice Test
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Avg score: 60% • Most missed: “Among On-policy and off-policy, which of the following target policy is not equa…”

Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
Reinforcement learning is based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error.

Reinforcement Learning Practice Test
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25 Questions

1. ___ is the policy that an agent is trying to learn?
2. Reinforcement learning is defined by the ____?
3. Which of the following gives the better final performance?
4. Which of the following elements of reinforcement learning imitates the behavior of the environment?
5. What are the Rewards of Reinforcement learning?
6. Reinforcement learning is a ____
7. In which of the following approaches of reinforcement learning, do we find the optimal value function?
8. How many types of reinforcement learning?
9. The agent's main objective is to ____the total number of rewards for good actions.?
10. Q-learning follows an on-policy learning algorithm or an off-policy learning algorithm?
11. What is the state of reinforcement learning?
12. What does Q stand for in Q-learning?
13. Which of the following is the practical example of reinforcement learning?
14. What do you mean by SARSA in reinforcement learning?
15. Which of the following algorithms will find the best course of action, based on the agent's current state, without using a model and off-policy reinforcement learning?
16. Among On-policy and off-policy, which of the following target policy is equal to behavior policy?
17. Which element in reinforcement learning defines the behavior of the agent?
18. What is an agent in reinforcement learning?
19. Which of the following type of policy is a learning algorithm in which the same policy is improved and evaluated?
20. What is DQN in reinforcement learning?
21. What is the environment in reinforcement learning?
22. Q-learning works on which equation?
23. Why do we use MDP in reinforcement learning?
24. Does reinforcement learning follow the concept of the Hit and try method?
25. P[St+1 | St ] = P[St +1 | S1,......, St], in this condition
What is the meaning of St?

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