Reinforcement Learning Essentials: AI MCQ Exam

Questions (30)

1. What is the main objective of reinforcement learning?

   • a) To label data for supervised learning tasks
   • b) To maximize the cumulative reward through learning from interaction with an environment
   • c) To find patterns in unlabeled data
   • d) To minimize classification error
   View Answer

   Correct To maximize the cumulative reward through learning from interaction with an environment

2. What does the term "agent" refer to in reinforcement learning?

   • a) The model used for supervised learning
   • b) The decision-maker that interacts with the environment to learn an optimal policy
   • c) The dataset used for training
   • d) The part of the system that handles input-output mapping
   View Answer

   Correct The decision-maker that interacts with the environment to learn an optimal policy

3. What is a "reward" in the context of reinforcement learning?

   • a) Feedback that measures the success or failure of an agent's action
   • b) The algorithm used to optimize the model
   • c) The dataset used for training the agent
   • d) A type of regularization technique
   View Answer

   Correct Feedback that measures the success or failure of an agent's action

4. Which of the following is an example of a reinforcement learning problem?

   • a) Image classification
   • b) Spam email detection
   • c) Robot navigation in a maze
   • d) Sentiment analysis
   View Answer

   Correct Robot navigation in a maze

5. What is the "state" in a reinforcement learning problem?

   • a) The input features used for supervised learning
   • b) The current representation of the environment as perceived by the agent
   • c) The algorithm used to optimize rewards
   • d) The hyperparameters of a model
   View Answer

   Correct The current representation of the environment as perceived by the agent

6. Which algorithm is commonly used in reinforcement learning to find the optimal policy?

   • a) Q-learning
   • b) k-means clustering
   • c) Support Vector Machines
   • d) Naive Bayes
   View Answer

   Correct Q-learning

7. What is the "exploration-exploitation trade-off" in reinforcement learning?

   • a) Choosing between exploring new actions or exploiting known actions to maximize reward
   • b) Balancing model complexity and computational efficiency
   • c) Deciding whether to use labeled or unlabeled data
   • d) Choosing between batch learning and online learning
   View Answer

   Correct Choosing between exploring new actions or exploiting known actions to maximize reward

8. What is the purpose of a discount factor in reinforcement learning?

   • a) To prioritize short-term rewards over long-term rewards
   • b) To balance exploration and exploitation
   • c) To stabilize the learning process
   • d) To determine how future rewards are weighted compared to immediate rewards
   View Answer

   Correct To determine how future rewards are weighted compared to immediate rewards

9. Which of the following is a common reinforcement learning algorithm based on value iteration?

   • a) Deep Q-Networks (DQN)
   • b) Random Forest
   • c) PCA
   • d) Logistic Regression
   View Answer

   Correct Deep Q-Networks (DQN)

10. What is "off-policy learning" in reinforcement learning?

    • a) Learning from actions that are not derived from the current policy
    • b) Learning directly from labeled data
    • c) Optimizing multiple policies simultaneously
    • d) Using a fixed policy throughout training
    View Answer

    Correct Learning from actions that are not derived from the current policy

11. What is the role of "experience replay" in reinforcement learning?

    • a) To increase the size of training datasets
    • b) To prevent overfitting in supervised learning tasks
    • c) To adjust model weights during gradient descent
    • d) To store and reuse past experiences to improve learning efficiency
    View Answer

    Correct To store and reuse past experiences to improve learning efficiency

12. Which reinforcement learning technique combines deep learning with Q-learning?

    • a) Deep Q-Network (DQN)
    • b) Gradient Boosting
    • c) Principal Component Analysis (PCA)
    • d) Recurrent Neural Networks (RNN)
    View Answer

    Correct Deep Q-Network (DQN)

13. What is a "policy gradient" method in reinforcement learning?

    • a) A clustering algorithm for large datasets
    • b) A technique that directly optimizes the policy by computing gradients with respect to the reward
    • c) A regularization method to reduce overfitting
    • d) A method for reducing dimensionality
    View Answer

    Correct A technique that directly optimizes the policy by computing gradients with respect to the reward

14. What is the purpose of a replay buffer in Deep Q-Learning?

    • a) To store past transitions for training and reduce correlation between data samples
    • b) To optimize the structure of the neural network
    • c) To manage the batch size during training
    • d) To calculate the loss function more efficiently
    View Answer

    Correct To store past transitions for training and reduce correlation between data samples

15. What is the primary advantage of using reinforcement learning in dynamic environments?

    • a) It adapts to changes in the environment and learns optimal policies through trial and error
    • b) It requires minimal computational resources
    • c) It eliminates the need for training data
    • d) It works only for static datasets
    View Answer

    Correct It adapts to changes in the environment and learns optimal policies through trial and error

16. What is the "reward signal" in reinforcement learning?

    • a) A type of activation function
    • b) A measure of computational efficiency
    • c) A parameter used for gradient descent
    • d) A scalar value that indicates the success or failure of an agent’s action in the environment
    View Answer

    Correct A scalar value that indicates the success or failure of an agent’s action in the environment

17. What is the main challenge of reinforcement learning?

    • a) Balancing exploration and exploitation to achieve optimal performance
    • b) Ensuring supervised learning accuracy
    • c) Managing large datasets
    • d) Simplifying the neural network structure
    View Answer

    Correct Balancing exploration and exploitation to achieve optimal performance

18. What is the "Bellman Equation" used for in reinforcement learning?

    • a) To compute the weights of a neural network
    • b) To update the value of a state based on its expected future rewards
    • c) To determine the discount factor
    • d) To optimize the exploration rate
    View Answer

    Correct To update the value of a state based on its expected future rewards

19. Which component is NOT part of a reinforcement learning system?

    • a) State
    • b) Reward
    • c) Label
    • d) Policy
    View Answer

    Correct Label

20. What is the main purpose of the "learning rate" in Q-learning?

    • a) To balance short-term and long-term rewards
    • b) To determine the agent's action based on a policy
    • c) To control how much new information overrides old information
    • d) To normalize the input data
    View Answer

    Correct To control how much new information overrides old information

21. What is an "episodic task" in reinforcement learning?

    • a) A task with a clear beginning and end
    • b) A task that continues indefinitely
    • c) A task with a fixed state space
    • d) A task where rewards are not discounted
    View Answer

    Correct A task with a clear beginning and end

22. Which of the following is an example of "continuous action space" in reinforcement learning?

    • a) Choosing from a set of predefined actions
    • b) Adjusting the throttle of a self-driving car
    • c) Selecting a menu option
    • d) Deciding between "yes" or "no"
    View Answer

    Correct Adjusting the throttle of a self-driving car

23. What is the role of a "critic" in the Actor-Critic method?

    • a) To update the policy directly
    • b) To estimate the value function and guide the actor
    • c) To execute actions in the environment
    • d) To adjust the learning rate dynamically
    View Answer

    Correct To estimate the value function and guide the actor

24. Which of the following best describes "Temporal Difference (TD) Learning"?

    • a) Learning by bootstrapping future rewards
    • b) Using labeled data for predictions
    • c) Computing gradients to optimize the model
    • d) Minimizing loss in supervised tasks
    View Answer

    Correct Learning by bootstrapping future rewards

25. What does "exploration" mean in reinforcement learning?

    • a) Trying new actions to discover their potential rewards
    • b) Using a fixed policy to maximize known rewards
    • c) Reducing the size of the state space
    • d) Increasing the discount factor
    View Answer

    Correct Trying new actions to discover their potential rewards

26. What is the purpose of a "target network" in Deep Q-Learning?

    • a) To normalize input data
    • b) To select the best action during exploration
    • c) To stabilize the training process by reducing oscillations
    • d) To compute the loss function
    View Answer

    Correct To stabilize the training process by reducing oscillations

27. Which method in reinforcement learning is most suitable for real-time applications?

    • a) Temporal Difference (TD) Learning
    • b) Monte Carlo Methods
    • c) Batch Gradient Descent
    • d) Clustering
    View Answer

    Correct Temporal Difference (TD) Learning

28. What does the term "convergence" refer to in reinforcement learning?

    • a) The state space becoming finite
    • b) The network weights becoming stable during training
    • c) The loss function reaching a minimum
    • d) The agent finding an optimal policy over time
    View Answer

    Correct The agent finding an optimal policy over time

29. What is the main limitation of reinforcement learning?

    • a) It requires extensive computational resources and time
    • b) It cannot handle continuous state spaces
    • c) It relies on labeled data for training
    • d) It only works for static environments
    View Answer

    Correct It requires extensive computational resources and time

30. What is the purpose of a "softmax policy" in reinforcement learning?

    • a) To select actions with equal probability
    • b) To assign probabilities to actions based on their Q-values
    • c) To maximize exploration at all times
    • d) To normalize input data
    View Answer

    Correct To assign probabilities to actions based on their Q-values