Quiz: RL Basics Quiz


RL Basics QuizOnline version Test your RL knowledge by tisha agarwal 1 What are the two core components in reinforcement learning? a Supervised Signal and Environment b Agent and Environment c Policy and Value d Neural Network and Dataset 2 In RL, what is the agent’s goal in simple terms? a Learn a policy mapping states to best actions b Minimize model size c Follow a fixed sequence of moves regardless of state d Maximize labels in data 3 Which statement best describes reinforcement learning learning style? a Learning from interaction with feedback (rewards/penalties) b Learning from predefined rules and instructions c Learning from unlabeled data only d Learning from labeled data only 4 What is a full interaction example in RL? a Input -> Output -> Error b Observation -> Decision -> Result c State -> Action -> Output d S0 (state) -> A0 (action) -> R1 (reward) -> S1 (next state) 5 Which domain is listed as an RL application in the slides? a Statistical optimization b Image compression c Self-driving cars d Spam filtering 6 How is RL different from supervised learning (SL) and unsupervised learning (USL)? a No supervisor and learning from environment rewards/penalties b It uses labeled targets for training c It only clusters data d It requires large unlabeled datasets always 7 Which of the following is a typical RL learning problem type? a Reward Based b Clustering c Regression d Classification 8 Name a real-world RL application mentioned for healthcare. a Genetic sequencing b Hospital scheduling c Adaptive treatment plans d Medical imaging segmentation 9 What does the 'policy' in RL refer to? a A fixed neural network architecture b A dataset of labeled actions c Mapping from each state to the best action d A random guess at each step Feedback 1 RL revolves around an agent acting within an environment; the agent’s actions affect the environment and vice versa. 2 The policy maps each state to the action that maximizes long-term reward. 3 RL learns by trial-and-error through rewards and penalties from the environment. 4 An interaction cycle includes observing state, taking an action, receiving reward, and moving to the next state. 5 Smart Vehicles example includes self-driving cars using RL to drive safely. 6 RL learns from interaction with the environment, not from labeled data or clustering alone. 7 RL is categorized as a reward-based learning problem. 8 RL in healthcare is used to tailor treatment plans based on patient response. 9 Policy defines the action choice given the current state to maximize cumulative reward.

1

What are the two core components in reinforcement learning?

a

Supervised Signal and Environment

b

Agent and Environment

c

Policy and Value

d

Neural Network and Dataset

2

In RL, what is the agent’s goal in simple terms?

a

Learn a policy mapping states to best actions

b

Minimize model size

c

Follow a fixed sequence of moves regardless of state

d

Maximize labels in data

3

Which statement best describes reinforcement learning learning style?

a

Learning from interaction with feedback (rewards/penalties)

b

Learning from predefined rules and instructions

c

Learning from unlabeled data only

d

Learning from labeled data only

4

What is a full interaction example in RL?

a

Input -> Output -> Error

b

Observation -> Decision -> Result

c

State -> Action -> Output

d

S0 (state) -> A0 (action) -> R1 (reward) -> S1 (next state)

5

Which domain is listed as an RL application in the slides?

a

Statistical optimization

b

Image compression

c

Self-driving cars

d

Spam filtering

6

How is RL different from supervised learning (SL) and unsupervised learning (USL)?

a

No supervisor and learning from environment rewards/penalties

b

It uses labeled targets for training

c

It only clusters data

d

It requires large unlabeled datasets always

7

Which of the following is a typical RL learning problem type?

a

Reward Based

b

Clustering

c

Regression

d

Classification

8

Name a real-world RL application mentioned for healthcare.

a

Genetic sequencing

b

Hospital scheduling

c

Adaptive treatment plans

d

Medical imaging segmentation

9

What does the 'policy' in RL refer to?

a

A fixed neural network architecture

b

A dataset of labeled actions

c

Mapping from each state to the best action

d

A random guess at each step

Feedback

1

RL revolves around an agent acting within an environment; the agent’s actions affect the environment and vice versa.

2

The policy maps each state to the action that maximizes long-term reward.

3

RL learns by trial-and-error through rewards and penalties from the environment.

4

An interaction cycle includes observing state, taking an action, receiving reward, and moving to the next state.

5

Smart Vehicles example includes self-driving cars using RL to drive safely.

6

RL learns from interaction with the environment, not from labeled data or clustering alone.

7

RL is categorized as a reward-based learning problem.

8

RL in healthcare is used to tailor treatment plans based on patient response.

9

Policy defines the action choice given the current state to maximize cumulative reward.

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RL Basics Quiz

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RL Basics QuizOnline version

by tisha agarwal

What are the two core components in reinforcement learning?

In RL, what is the agent’s goal in simple terms?

Which statement best describes reinforcement learning learning style?

What is a full interaction example in RL?

Which domain is listed as an RL application in the slides?

How is RL different from supervised learning (SL) and unsupervised learning (USL)?

Which of the following is a typical RL learning problem type?

Name a real-world RL application mentioned for healthcare.

What does the 'policy' in RL refer to?

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