MCQs

Disclaimer:

Educational Use Only:

The website links provided are to be used strictly for educational and knowledge-gaining purposes only.

No Misuse:

Do not misuse the links or the information contained therein for any commercial, illegal, or unethical activities.

Respect for Websites and Businesses:

Respect the intellectual property and business interests of the websites and their owners. Do not attempt to alter, copy, or redistribute content without proper authorization.

Acknowledge Value of Knowledge:

Recognize and value the knowledge and resources shared by these websites. Use the information responsibly and ethically.

No Endorsement:

The provision of these links does not imply endorsement of the linked sites, their content, or their products and services. Users are responsible for evaluating the suitability and accuracy of the information.

User Responsibility:

By accessing these links, you agree to use the information at your own risk and to comply with all applicable laws and website terms of use.

Overall MCQs for Reference

Which core concept does Deep Q-Learning combine?
a. Neural Networks and Supervised Learning
b. Q-Learning and Deep Neural Networks
c. Clustering and Decision Trees
d. Genetic Algorithms and Backpropagation
Answer: bupgrad+1
What is the function of the Q-value in Deep Q-Learning?
a. Measures the speed of convergence
b. Predicts the value of a state-action pair
c. Calculates gradient descent
d. Monitors data normalization
Answer: bnature+1
What innovation allows DQN to stabilize training?
a. Policy Swapping
b. Experience Replay
c. Batch Normalization
d. Data Augmentation
Answer: bwizzdev+1
Which type of problem is Deep Q-Learning most suited for?
a. Unsupervised Clustering
b. Sequence Prediction
c. Large-scale state-action problems
d. Label Propagation
Answer: cdiva-portal+1
What does the “Deep” in Deep Q-Learning refer to?
a. Deep Forests
b. Deep Search Trees
c. Deep Neural Networks
d. Deep Data Pipelines
Answer: cupgrad+1
What is Experience Replay in DQN?
a. Data compression step
b. Model checkpoint saving
c. Reusing past experiences for learning
d. Hyperparameter tuning
Answer: cnature+1
How does the target network in DQN improve stability?
a. It updates synchronously with the main network
b. It accumulates gradients
c. It uses delayed updates for more stable targets
d. It stores raw inputs
Answer: cwizzdev+1
Which field is NOT a common application of Deep Q-Learning?
a. Robotics
b. Healthcare
c. Textbook publishing
d. Finance
Answer: cgeeksforgeeks+1
What type of learning does Deep Q-Learning fall under?
a. Supervised
b. Reinforcement
c. Semi-supervised
d. Unsupervised
Answer: binoxoft+1
Which of the following games gained attention using Deep Q-Learning?
a. Chess
b. Sudoku
c. Atari
d. Tetris
Answer: cgeeksforgeeks+1
What does a DQN agent receive from the environment after each action?
a. A new policy
b. A reward and a next state
c. An input sequence
d. A loss function
Answer: bgeeksforgeeks+1
What challenge does Deep Q-Learning uniquely address compared to tabular Q-Learning?
a. Poor scalability
b. High variance
c. Large or continuous state spaces
d. Shallow structures
Answer: cdiva-portal+1
The standard loss function in DQN involves:
a. Cross-entropy
b. Mean squared error between Q-values
c. Binary classification loss
d. Negative log-likelihood
Answer: bnature+1
Which component selects the next action in the DQN algorithm?
a. Loss minimizer
b. Policy network
c. Q-network
d. Replay buffer
Answer: cupgrad+1
What does epsilon represent in epsilon-greedy exploration?
a. Exploration duration
b. Probability of choosing a random action
c. State size
d. Learning rate
Answer: binoxoft+1
A reduced epsilon value eventually leads to:
a. Less exploitation
b. More random moves
c. More exploitation
d. Increased overfitting
Answer: cwizzdev+1
Which technique is used to break correlation among samples in DQN?
a. Batch normalization
b. Experience replay buffer
c. Dropout layer
d. Max pooling
Answer: bnature+1
Which is a drawback of Deep Q-Learning?
a. Only works on static data
b. Needs a lot of labeled data
c. Can be unstable to train
d. Requires no hyperparameters
Answer: cdiva-portal+1
Which is an extension to DQN that allows learning in continuous action spaces?
a. Categorical DQN
b. Double DQN
c. Deep Deterministic Policy Gradient (DDPG)
d. Prioritized DQN
Answer: caimcqs+1
How does Double DQN improve standard DQN?
a. Adds policy gradients
b. Uses two networks to reduce overestimation bias
c. Uses extra layers for precision
d. Removes target networks
Answer: bdiva-portal+1
What is prioritized experience replay?
a. Sampling recent experiences only
b. Sampling experiences based on learning value
c. Ignoring uncommon experiences
d. Sorting experiences randomly
Answer: bwizzdev+1
In DQN, the target network’s weights are:
a. Updated after every action
b. Never updated
c. Periodically copied from the main network
d. Computed through dropout
Answer: cnature+1
DQN was first popularized in which high-profile research?
a. Google BERT
b. OpenAI GPT
c. DeepMind Atari
d. IBM Watson
Answer: cgeeksforgeeks+1
What is bootstrapping in DQN?
a. Using past weights for training
b. Estimating value from current predictions
c. Resampling data
d. Training with multiple GPUs
Answer: bwizzdev+1
What is the benefit of using convolutional neural networks (CNNs) in DQN?
a. Faster weight initialization
b. Better handling of pixel-based input
c. Reduced memory usage
d. Simpler architectures
Answer: bupgrad+1
Which of the following is NOT a variant of Q-learning?
a. Double Q-learning
b. Prioritized replay Q-learning
c. Markov Q-learning
d. Dueling Q-learning
Answer: cdiva-portal+1
What is the main advantage of dueling architectures in DQN?
a. Faster GPU performance
b. Separately estimates value and advantage functions
c. Reduces the learning rate
d. Removes need for experience replay
Answer: bwizzdev+1
The reward maximization objective in Deep Q-Learning is often called:
a. Policy gradient
b. Objective function
c. Bellman optimality
d. Lagrangian function
Answer: cnature+1
In Deep Q-Learning, what is typically minimized during training?
a. Actor loss
b. Cross-entropy
c. Temporal difference error
d. Policy entropy
Answer: cdiva-portal+1
In which industry is Deep Q-Learning impacting energy optimization?
a. Retail
b. Smart energy management
c. Cosmetics
d. Printing services
Answer: bwizzdev
What kind of policy does a Deep Q-Network learn?
a. Model-free
b. Model-based
c. Stochastic only
d. Random walk
Answer: aupgrad+1
Advantage updating in robust Q-learning helps with:
a. Weight initialization
b. Time discretization issues
c. Label smoothing
d. Batch size optimization
Answer: bproceedings.mlr
Which of the following is NOT a requirement for Deep Q-Learning?
a. A differentiable value approximation
b. A reward signal
c. A model of the environment
d. An exploration strategy
Answer: cnature+1
What input does a Deep Q-Network process to select actions in games?
a. Feature vectors only
b. Raw pixel states
c. Pre-extracted labels
d. Reward tables
Answer: bgeeksforgeeks+1
Markov Decision Process (MDP) assumption is important in DQN because:
a. Rewards are always positive
b. Past actions are ignored
c. Future states depend only on current state and action
d. Batch processing is enabled
Answer: cinoxoft+1
What prevents overfitting in DQN models?
a. Dropout and regularization
b. Aggressive exploration
c. Smaller replay buffers
d. More hidden layers
Answer: ainoxoft+1
In which environment has Deep Q-Learning achieved superhuman performance?
a. Rubik’s Cube
b. Atari video games
c. Chess
d. Recommender systems
Answer: bgeeksforgeeks+1
What is a key limitation of Deep Q-Learning?
a. Limited to discrete action spaces
b. Only works on text
c. Requires labeled datasets
d. Ignores rewards
Answer: aaimcqs+1
Deep Q-Learning is referred to as “model-free” because:
a. It builds a world model
b. It uses Monte Carlo sampling
c. It doesn’t require an explicit environment model
d. It always randomizes
Answer: cdiva-portal+1
Which step updates the value of each state-action pair in DQN?
a. Gradient clipping
b. Backpropagation using TD error
c. Minibatch normalization
d. Entropy regularization
Answer: bnature+1
To encourage exploration, DQN often uses:
a. Greedy selection only
b. Random walk
c. Epsilon-greedy policy
d. Policy gradient with entropy
Answer: cinoxoft+1
What optimizes the Q-function in Deep Q-Learning?
a. RMSprop or Adam optimizer
b. ID3 algorithm
c. Naïve Bayes classifier
d. AdaBoost
Answer: aupgrad+1
What is the ultimate goal of Deep Q-Learning for an agent?
a. Minimize loss
b. Maximize expected cumulative rewards
c. Maximize randomness
d. Reduce state space
Answer: bupgrad+1
What makes DQN more scalable than tabular Q-Learning?
a. It uses smaller buffers
b. It leverages neural networks for function approximation
c. It focuses on one-step returns
d. It uses final state only
Answer: bdiva-portal+1
Which variant helps DQN handle overestimation bias?
a. Double DQN
b. Dueling DQN
c. Prioritized DQN
d. Recurrent DQN
Answer: anature+1
What is commonly used as input for DQNs in vision-based tasks?
a. Audio signals
b. Image frames
c. Raw text files
d. Symbolic data
Answer: bgeeksforgeeks+1
Why is a target network important in DQN?
a. Enables distributed training
b. Reduces oscillations and divergence
c. Boosts batch size
d. Changes action set
Answer: bwizzdev+1
What is one popular library for implementing DQNs in Python?
a. Pandas
b. Gym
c. Matplotlib
d. NumPy
Answer: binoxoft+1
What learning paradigm does DQN use?
a. On-policy
b. Off-policy
c. Unsupervised
d. Transfer learning
Answer: bproceedings.mlr+1
Which application leverages DQN for optimal resource allocation?
a. Cloud storage
b. Smart energy management
c. Email sorting
d. Signature detection
Answer: binoxoft+1

Blog

Systemethic Technologies

Leave a Reply Cancel reply

Useful links

Other Terms & Policies

Contact Info