MCQs

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Overall MCQs for Reference

1. What is the primary goal of reinforcement learning?
   a. To classify data
   b. To maximize cumulative reward
   c. To minimize error
   d. To cluster similar data points
   Answer: b. To maximize cumulative rewardtechtarget+2​
2. Which of the following is NOT a component of reinforcement learning?
   a. Agent
   b. Environment
   c. Labeled dataset
   d. Reward
   Answer: c. Labeled datasetaws.amazon+2​
3. In reinforcement learning, what does the agent interact with?
   a. Dataset
   b. Environment
   c. Model
   d. Algorithm
   Answer: b. Environmentgeeksforgeeks+2​
4. What is the term for the feedback received by the agent after taking an action?
   a. Cost
   b. Reward
   c. Loss
   d. Gradient
   Answer: b. Rewardmathworks+2​
5. Which of the following is a practical example of reinforcement learning?
   a. Image classification
   b. Driverless cars
   c. Spam detection
   d. Sentiment analysis
   Answer: b. Driverless carsincludehelp+1​
6. What is the main objective of a reinforcement learning agent?
   a. To minimize the number of actions
   b. To maximize the total number of rewards
   c. To reduce computational complexity
   d. To avoid all penalties
   Answer: b. To maximize the total number of rewardsincludehelp+2​
7. What is an agent in reinforcement learning?
   a. The environment
   b. The reward function
   c. An entity that explores the environment
   d. The algorithm used
   Answer: c. An entity that explores the environmentmilvus+2​
8. Which of the following is a type of reinforcement learning approach?
   a. Supervised learning
   b. Value-based
   c. Unsupervised learning
   d. Semi-supervised learning
   Answer: b. Value-basedstanford+1​
9. In which approach do we find the optimal value function?
   a. Policy-based
   b. Value-based
   c. Model-based
   d. Model-free
   Answer: b. Value-basedlinkedin+1​
10. How many types of policy-based approaches are there in reinforcement learning?
    a. 1
    b. 2
    c. 3
    d. 4
    Answer: b. 2linkedin+2​
11. In which approach is a virtual model created for the environment?
    a. Model-based
    b. Model-free
    c. Value-based
    d. Policy-based
    Answer: a. Model-basedml-compiled.readthedocs+1​
12. What is the main objective of the agent in reinforcement learning?
    a. To minimize risk
    b. To maximize cumulative reward
    c. To reduce variance
    d. To optimize speed
    Answer: b. To maximize cumulative rewardtechtarget+2​
13. On which element is the reward that an agent can expect dependent?
    a. Action
    b. State
    c. Value function
    d. Policy
    Answer: c. Value functionincompleteideas+2​
14. Which element imitates the behavior of the environment?
    a. Agent
    b. Reward
    c. Policy
    d. Model of the environment
    Answer: d. Model of the environmentml-compiled.readthedocs+1​
15. In which type of reinforcement learning do we add something that increases the likelihood of repeating expected behavior?
    a. Positive reinforcement
    b. Negative reinforcement
    c. Punishment
    d. Neutral reinforcement
    Answer: a. Positive reinforcementtestbook+2​
16. How is the agent state represented in reinforcement learning?
    a. Discount state
    b. Discount factor
    c. Markov state
    d. Reward state
    Answer: c. Markov statestudocu+1​
17. What is the role of the value function in reinforcement learning?
    a. To estimate the expected cumulative future rewards
    b. To define the agent’s actions
    c. To model the environment
    d. To assign rewards
    Answer: a. To estimate the expected cumulative future rewardsmilvus+2​
18. Which of the following is a type of policy in reinforcement learning?
    a. Deterministic
    b. Supervised
    c. Unsupervised
    d. Semi-supervised
    Answer: a. Deterministicgeeksforgeeks+2​
19. What is a stochastic policy in reinforcement learning?
    a. Always selects the same action for a given state
    b. Selects actions based on a probability distribution
    c. Uses labeled data
    d. Avoids all penalties
    Answer: b. Selects actions based on a probability distributionbaeldung+2​
20. What is the Bellman equation used for in reinforcement learning?
    a. To update the policy
    b. To estimate the value function
    c. To model the environment
    d. To assign rewards
    Answer: b. To estimate the value functionlinkedin+1​
21. Which of the following is an example of a value-based method?
    a. Q-learning
    b. REINFORCE
    c. Actor-Critic
    d. SARSA
    Answer: a. Q-learningwikipedia+1​
22. What is the difference between on-policy and off-policy learning?
    a. On-policy uses the same policy for exploration and learning
    b. Off-policy uses different policies for exploration and learning
    c. Both a and b
    d. None of the above
    Answer: c. Both a and bml-compiled.readthedocs​
23. What is the main idea behind policy gradient methods?
    a. To directly learn the value function
    b. To directly learn the policy
    c. To model the environment
    d. To assign rewards
    Answer: b. To directly learn the policygeeksforgeeks+1​
24. Which of the following is a model-free reinforcement learning algorithm?
    a. Q-learning
    b. Dynamic programming
    c. Value iteration
    d. Policy iteration
    Answer: a. Q-learningwikipedia+1​
25. What is the main advantage of using a model-based approach?
    a. Faster learning
    b. Requires less data
    c. Can predict future states
    d. Simpler implementation
    Answer: c. Can predict future statesml-compiled.readthedocs​
26. What is the main disadvantage of model-free approaches?
    a. Slower learning
    b. Requires more data
    c. Cannot predict future states
    d. Complex implementation
    Answer: c. Cannot predict future statesml-compiled.readthedocs​
27. What is the role of the discount factor in reinforcement learning?
    a. To determine the agent’s actions
    b. To weigh future rewards
    c. To model the environment
    d. To assign rewards
    Answer: b. To weigh future rewardsaws.amazon+1​
28. What is the main difference between supervised and reinforcement learning?
    a. Supervised learning uses labeled data
    b. Reinforcement learning uses rewards
    c. Both a and b
    d. None of the above
    Answer: c. Both a and bgyansetu+1​
29. Which of the following is a key concept in reinforcement learning?
    a. Trial and error
    b. Labeled data
    c. Clustering
    d. Classification
    Answer: a. Trial and errorspinningup.openai+1​
30. What is the main purpose of the reward function?
    a. To guide the agent’s learning
    b. To model the environment
    c. To assign actions
    d. To reduce variance
    Answer: a. To guide the agent’s learningtechtarget+1​
31. What is the main difference between deterministic and stochastic policies?
    a. Deterministic always selects the same action
    b. Stochastic selects actions based on probability
    c. Both a and b
    d. None of the above
    Answer: c. Both a and blinkedin+2​
32. What is the main idea behind the Markov decision process?
    a. The next state depends only on the current state
    b. The next state depends on all previous states
    c. The next state depends on the reward
    d. The next state depends on the action
    Answer: a. The next state depends only on the current statewikipedia+1​
33. What is the main purpose of the action-value function Q(s, a)?
    a. To estimate the expected cumulative future rewards
    b. To model the environment
    c. To assign rewards
    d. To reduce variance
    Answer: a. To estimate the expected cumulative future rewardsmilvus+1​
34. What is the main difference between value iteration and policy iteration?
    a. Value iteration updates the value function
    b. Policy iteration updates the policy
    c. Both a and b
    d. None of the above
    Answer: c. Both a and blinkedin​
35. What is the main purpose of the policy evaluation step?
    a. To estimate the value of each state under a given policy
    b. To update the policy
    c. To model the environment
    d. To assign rewards
    Answer: a. To estimate the value of each state under a given policylinkedin​
36. What is the main purpose of the policy improvement step?
    a. To suggest actions that lead to higher expected rewards
    b. To update the value function
    c. To model the environment
    d. To assign rewards
    Answer: a. To suggest actions that lead to higher expected rewardslinkedin​
37. What is the main idea behind the REINFORCE algorithm?
    a. To directly learn the policy
    b. To estimate the value function
    c. To model the environment
    d. To assign rewards
    Answer: a. To directly learn the policygeeksforgeeks+1​
38. What is the main difference between Q-learning and SARSA?
    a. Q-learning is off-policy
    b. SARSA is on-policy
    c. Both a and b
    d. None of the above
    Answer: c. Both a and bwikipedia+1​
39. What is the main purpose of the exploration-exploitation trade-off?
    a. To balance between trying new actions and using known good actions
    b. To model the environment
    c. To assign rewards
    d. To reduce variance
    Answer: a. To balance between trying new actions and using known good actionsaws.amazon+1​
40. What is the main idea behind the epsilon-greedy strategy?
    a. To select the best-known action most of the time
    b. To select random actions sometimes
    c. Both a and b
    d. None of the above
    Answer: c. Both a and btechtarget+1​
41. What is the main purpose of the discount factor gamma?
    a. To weigh future rewards
    b. To model the environment
    c. To assign rewards
    d. To reduce variance
    Answer: a. To weigh future rewardsaws.amazon+1​
42. What is the main difference between episodic and continuous tasks?
    a. Episodic tasks have a clear end
    b. Continuous tasks do not have a clear end
    c. Both a and b
    d. None of the above
    Answer: c. Both a and btechtarget+1​
43. What is the main purpose of the reward shaping technique?
    a. To guide the agent’s learning
    b. To model the environment
    c. To assign rewards
    d. To reduce variance
    Answer: a. To guide the agent’s learningaws.amazon+1​
44. What is the main idea behind the actor-critic method?
    a. To combine value-based and policy-based approaches
    b. To model the environment
    c. To assign rewards
    d. To reduce variance
    Answer: a. To combine value-based and policy-based approacheswikipedia+1​
45. What is the main purpose of the eligibility trace?
    a. To assign credit to past actions
    b. To model the environment
    c. To assign rewards
    d. To reduce variance
    Answer: a. To assign credit to past actionstechtarget+1​
46. What is the main difference between Monte Carlo and temporal difference methods?
    a. Monte Carlo uses complete episodes
    b. Temporal difference uses partial episodes
    c. Both a and b
    d. None of the above
    Answer: c. Both a and baws.amazon+1​
47. What is the main purpose of the softmax action selection?
    a. To select actions based on a probability distribution
    b. To model the environment
    c. To assign rewards
    d. To reduce variance
    Answer: a. To select actions based on a probability distributiontechtarget+1​
48. What is the main idea behind the deep Q-network (DQN)?
    a. To use neural networks for Q-learning
    b. To model the environment
    c. To assign rewards
    d. To reduce variance
    Answer: a. To use neural networks for Q-learningwikipedia+1​
49. What is the main purpose of the experience replay buffer?
    a. To store past experiences
    b. To model the environment
    c. To assign rewards
    d. To reduce variance
    Answer: a. To store past experiencesaws.amazon+1​
50. What is the main idea behind the trust region policy optimization (TRPO)?
    a. To optimize the policy within a trust region
    b. To model the environment
    c. To assign rewards
    d. To reduce variance
    Answer: a. To optimize the policy within a trust regionml-compiled.readthedocs+1​

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4. https://www.ibm.com/think/topics/reinforcement-learning
5. https://www.mathworks.com/help/reinforcement-learning/ug/create-agents-for-reinforcement-learning.html
6. https://www.includehelp.com/mcq/reinforcement-learning-mcqs.aspx
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8. https://stanford.edu/~cpiech/cs221/fall12/exams/final_practice.pdf
9. https://www.linkedin.com/pulse/reinforcement-learning-value-function-detailed-analysis-prema-p
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11. https://www.geeksforgeeks.org/machine-learning/what-is-policy-in-reinforcement-learning/
12. https://ml-compiled.readthedocs.io/en/main/rl_types_of_algorithms.html
13. http://incompleteideas.net/book/first/ebook/node34.html
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16. https://en.wikipedia.org/wiki/Reinforcement_learning
17. https://milvus.io/ai-quick-reference/what-is-a-value-function-in-rl
18. https://www.baeldung.com/cs/rl-deterministic-vs-stochastic-policies
19. https://www.gyansetu.in/blog/machine-learning-mcq-interview-preparation/
20. https://spinningup.openai.com/en/latest/spinningup/rl_intro.html
21. https://aimcqs.com/reinforcement-learning
22. https://www.kaggle.com/getting-started/198733
23. https://courses.grainger.illinois.edu/ece448/sp2025/exams/ece448sp2024practice3.pdf
24. https://www.opit.com/magazine/reinforcement-learning-2/
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32. https://matoffo.com/reinforcement-learning-agents-explained/
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34. https://towardsdatascience.com/reinforcement-learning-part-7-introduction-to-value-function-approximation-2e22495f7008/
35. https://www.youtube.com/watch?v=zH7Od0JzSrI