MCQs

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Overall MCQs for Reference

Value approximation in reinforcement learning is primarily used to:
a. Reduce memory requirements in large state spaces
b. Increase speed of search algorithms
c. Simplify optimization in supervised learning
d. None of the above
Answer: aaionlinecourse+2
Which technique is NOT commonly used for value function approximation?
a. Linear functions
b. Neural networks
c. Decision trees
d. Hash tables
Answer: deasyexamnotes+2
Linear value function approximation expresses the value as:
a. A fixed table lookup
b. A linear combination of features
c. The maximum of all possible rewards
d. A random guess
Answer: bcmutschler+1
The primary motivation for using value function approximation is:
a. Handling small state spaces
b. Handling large or continuous state spaces
c. Removing the need for rewards
d. Avoiding exploration
Answer: baionlinecourse+1
In reinforcement learning, what does a feature vector represent for value approximation?
a. A random variable
b. A compact representation of state
c. An action sequence
d. A list of rewards
Answer: bcmutschler
Which neural network-based algorithm approximates the Q-value in complex environments?
a. Dijkstra’s Algorithm
b. Deep Q-Network (DQN)
c. Breadth-First Search
d. Policy Iteration
Answer: baimcqs+1
What is the main drawback of using a lookup table for value functions in large state spaces?
a. Too slow
b. Too inaccurate
c. Requires too much memory
d. Not precise
Answer: caionlinecourse+1
What is meant by “generalization” in value function approximation?
a. Overfitting to training data
b. Applying learned values to unseen states
c. Ignoring the rewards
d. Always predicting zero
Answer: bcmutschler+1
The weights in linear value function approximation are updated using:
a. Least squares regression
b. Random assignment
c. Genetic algorithms
d. None of the above
Answer: aeasyexamnotes+2
In Temporal Difference (TD) learning with function approximation, the update uses:
a. The exact difference between true value and current estimate
b. The difference between successive estimates and observed reward
c. The minimum value only
d. No learning
Answer: baimcqs+2
Which basis function is NOT commonly used in value approximation?
a. Polynomial
b. Fourier series
c. Radial basis function
d. Fibonacci sequence
Answer: daionlinecourse
What is the bootstrapping property in value approximation?
a. Always using full episode rewards
b. Updating value estimates based on other estimates
c. Ignoring transitions
d. Using deterministic updates
Answer: beasyexamnotes+1
Which is an advantage of neural-network-based value function approximation?
a. Always converges
b. Handles non-linear relationships
c. Requires no data
d. No risk of overfitting
Answer: baionlinecourse+1
Overfitting in neural network value function approximation is primarily caused by:
a. Too few parameters
b. Too much data
c. Too many parameters relative to data
d. Only using linear functions
Answer: cexamradar+1
Which algorithm updates its value estimate using both the observed reward and the difference between successive estimates?
a. Monte Carlo
b. Temporal Difference
c. Policy Gradient
d. Random Forest
Answer: baimcqs+1
In policy approximation, what is typically updated?
a. State values only
b. Action preferences or policy parameters
c. Discount factors
d. Reward tables
Answer: beasyexamnotes+1
Deep reinforcement learning uses neural networks as:
a. Exact value tables
b. Function approximators for value or policy
c. Only classifiers
d. Static dictionaries
Answer: bjavaguides+2
The Bellman equation in value approximation helps to:
a. Define optimal policy recursion
b. Estimate gradients
c. Eliminate exploration
d. None of the above
Answer: astudocu+2
Generalization error in value function approximation refers to:
a. Error on seen states
b. Error on unseen states
c. Only reward prediction error
d. Parameter initialization error
Answer: bcmutschler+1
Which learning rate condition is important for convergence in value function approximation?
a. Fixed at zero
b. Diminishing over time
c. Randomly reset
d. No effect on convergence
Answer: baionlinecourse
Value function approximation enables learning in which type of problem?
a. Small, discrete state spaces
b. Large, continuous, or high-dimensional state spaces
c. Static classification
d. None of the above
Answer: beasyexamnotes+2
Least Squares Temporal Difference (LSTD) is best described as:
a. An unsupervised classification algorithm
b. A regression technique for value function approximation
c. A clustering method
d. A greedy policy search method
Answer: baionlinecourse+1
Which of these is NOT a value function approximation method?
a. Table lookup
b. Linear regression
c. Neural networks
d. Radial basis function networks
Answer: acmutschler+1
Feature selection in value function approximation is important for:
a. Reducing overfitting and increasing interpretability
b. Making the algorithm run slower
c. Increasing randomness
d. Ignoring policy
Answer: acmutschler+1
The use of function approximation in RL may lead to which issue?
a. Underfitting
b. Instability or divergence
c. No effect
d. Always optimal solutions
Answer: beasyexamnotes+2
Which technique is typically used to minimize the error in value function approximation?
a. Cross-entropy
b. Mean squared error
c. Maximum likelihood only
d. Harmonic mean
Answer: baionlinecourse+1
Q-learning with value function approximation updates which components?
a. Policy only
b. Q-value estimates based on features and weights
c. Rewards only
d. Transition probabilities
Answer: baimcqs+1
Generalization in function approximation can cause:
a. Overfitting
b. Underfitting
c. Both overfitting and underfitting
d. None of the above
Answer: cexamradar+1
Combining linear and nonlinear basis functions in value approximation achieves:
a. No improvement
b. Increased flexibility and representational power
c. Always worse performance
d. No change in results
Answer: baionlinecourse
Which RL method may use function approximation for learning policy parameters directly?
a. Policy Gradient
b. Pure random search
c. Uninformed search
d. Enumeration
Answer: aaionlinecourse+2
What is batch reinforcement learning?
a. Online update with each sample
b. Updating policies and values in batches using function approximation
c. Ignoring value functions
d. Single-step lookahead
Answer: baionlinecourse
Eligibility traces in TD(λ) learning with value approximation are used to:
a. Store rewards
b. Accelerate learning by assigning credit
c. Control exploration
d. Eliminate policies
Answer: baimcqs+1
A primary challenge in value function approximation with deep learning is:
a. Too small datasets
b. Instability in training
c. Always converges
d. Fully supervised targets
Answer: baionlinecourse+1
The target for updating a value function in TD(0) with function approximation is:
a. The average of all rewards
b. The immediate reward plus discounted next value estimate
c. Always zero
d. Previous rewards only
Answer: baimcqs+1
Value function approximation is essential for:
a. Gridworlds with 10 or fewer states
b. Real-world or high-dimensional tasks
c. Static logic inference
d. Updating alpha-beta pruning
Answer: bcmutschler+1
What does “bootstrapping” mean in the context of value approximation?
a. Ignoring intermediate states
b. Using current estimates to update values
c. Starting from random weights only
d. None of the above
Answer: beasyexamnotes+1
The discount factor ( ) in value function approximation controls:
a. Exploration probability
b. Weight decay
c. Future reward importance
d. Batch size
Answer: cstudocu+1
In deep RL, the popular loss function for value approximation is:
a. Categorical cross-entropy
b. Mean squared error between predicted and target values
c. Kullback-Leibler divergence
d. Hinge loss
Answer: baionlinecourse+1
Which is a sign of underfitting in value function approximation?
a. High training and test error
b. Low training, but high test error
c. Only low training error
d. None of these
Answer: aexamradar
A deep Q-network directly approximates:
a. The reward at each step
b. The Q-value as a function of state and action
c. The policy gradient
d. The model transition probabilities
Answer: baionlinecourse+1
When is table-based representation preferable to value function approximation?
a. When the state-action space is small
b. For very high dimensions
c. Always
d. Never
Answer: acmutschler+1
The function approximation method that fits a neural network to state-action-reward transitions is:
a. SARSA
b. Deep Q-learning
c. Linear regression
d. Tabular methods
Answer: baionlinecourse+1
Which term best describes the process of mapping from state features to a value?
a. Value function approximation
b. Random search
c. Adversarial training
d. Batch normalization
Answer: acmutschler+1
What is the effect of a poorly chosen feature set in linear value approximation?
a. Better generalization
b. Poor accuracy and generalization
c. Always improves results
d. No effect
Answer: baionlinecourse+1
Deep Q-Networks are associated with which significant achievement in AI?
a. Winning chess against Kasparov
b. Mastering Atari video games
c. Translating languages
d. Speech recognition
Answer: baionlinecourse
Q-function approximation is crucial for:
a. Only policy-gradient methods
b. Large or continuous action spaces
c. Grid search
d. Histogram equalization
Answer: baionlinecourse+1
The Bellman error in value function approximation refers to:
a. The residual of value estimates after value updates
b. The maximum reward
c. Cumulative return
d. The discount rate
Answer: aeasyexamnotes+1
Non-linear function approximation is most useful when:
a. The problem exhibits complex, non-linear relationships
b. The data is perfectly linear
c. A table lookup is fastest
d. A single variable decides outcomes
Answer: aaionlinecourse+1
Value approximation can be unstable if:
a. Learning rate is too high
b. Features are highly correlated
c. Network is too deep
d. All of the above
Answer: dexamradar+2
The ultimate goal of approximating a value function in reinforcement learning is:
a. To compute the best possible actions (optimal policy)
b. To avoid using algorithms
c. To randomly explore
d. To remove agents
Answer: aeasyexamnotes+2

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