Q. In reinforcement learning, what is an 'agent'?
A.
A data point in a dataset
B.
A model that predicts outcomes
C.
An entity that takes actions in an environment
D.
A method for evaluating performance
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Solution
An agent is the entity that interacts with the environment by taking actions to achieve a goal.
Correct Answer:
C
— An entity that takes actions in an environment
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Q. In which real-world application is reinforcement learning commonly used?
A.
Image classification
B.
Natural language processing
C.
Game playing
D.
Data clustering
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Solution
Reinforcement learning is widely used in game playing, such as training agents to play chess or video games, where they learn optimal strategies through trial and error.
Correct Answer:
C
— Game playing
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Q. What does the term 'environment' refer to in reinforcement learning?
A.
The dataset used for training
B.
The external system the agent interacts with
C.
The algorithm used for learning
D.
The performance metrics
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Solution
The environment is the external system with which the agent interacts and receives feedback in the form of rewards.
Correct Answer:
B
— The external system the agent interacts with
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Q. What is 'discount factor' in reinforcement learning?
A.
A measure of the agent's performance
B.
A value that determines the importance of future rewards
C.
A method for clustering actions
D.
A technique for data normalization
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Solution
The discount factor determines how much future rewards are valued compared to immediate rewards, influencing the agent's decision-making.
Correct Answer:
B
— A value that determines the importance of future rewards
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Q. What is 'exploration' in the context of reinforcement learning?
A.
Using known information to make decisions
B.
Trying new actions to discover their effects
C.
Evaluating the performance of the agent
D.
Clustering similar actions
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Solution
Exploration involves trying new actions to discover their potential rewards, which is essential for learning in uncertain environments.
Correct Answer:
B
— Trying new actions to discover their effects
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Q. What is the difference between 'on-policy' and 'off-policy' learning?
A.
On-policy learns from the current policy, off-policy learns from a different policy
B.
On-policy uses supervised learning, off-policy uses unsupervised learning
C.
On-policy is faster than off-policy
D.
There is no difference
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Solution
On-policy learning updates the policy based on actions taken by the current policy, while off-policy learning can learn from actions taken by a different policy.
Correct Answer:
A
— On-policy learns from the current policy, off-policy learns from a different policy
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Q. What is the primary goal of reinforcement learning?
A.
To classify data into categories
B.
To predict future outcomes based on past data
C.
To learn a policy that maximizes cumulative reward
D.
To cluster similar data points together
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Solution
Reinforcement learning focuses on learning a policy that maximizes the cumulative reward over time through interactions with the environment.
Correct Answer:
C
— To learn a policy that maximizes cumulative reward
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Q. What is the role of 'reward' in reinforcement learning?
A.
To measure the accuracy of predictions
B.
To provide feedback to the agent about its actions
C.
To cluster data points
D.
To evaluate the model's performance
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Solution
The reward provides feedback to the agent about the effectiveness of its actions, guiding it towards optimal behavior.
Correct Answer:
B
— To provide feedback to the agent about its actions
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Q. Which algorithm is commonly associated with reinforcement learning?
A.
K-Means Clustering
B.
Q-Learning
C.
Linear Regression
D.
Principal Component Analysis
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Solution
Q-Learning is a widely used algorithm in reinforcement learning that helps the agent learn the value of actions in different states.
Correct Answer:
B
— Q-Learning
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Q. Which of the following is a common method used to represent the policy in reinforcement learning?
A.
Decision Trees
B.
Neural Networks
C.
Support Vector Machines
D.
Linear Regression
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Solution
Neural networks are often used to represent the policy in reinforcement learning due to their ability to approximate complex functions.
Correct Answer:
B
— Neural Networks
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