Machine Learning Reinforcement Learning

Question 1

What is the primary goal of a reinforcement learning agent?

Accepted Answer

To maximize cumulative reward over time

Answer

To label training data accurately

Answer

To minimize classification error on a test set

Answer

To cluster similar data points together

Question 2

What does the reward signal represent in reinforcement learning?

Accepted Answer

Immediate scalar feedback indicating how good an action was

Answer

The accuracy of predictions made by the model

Answer

The gradient of the loss function

Answer

The probability of selecting a given action

Question 3

In reinforcement learning, what is an 'episode'?

Accepted Answer

A complete sequence from the initial state to a terminal state

Answer

A single mini-batch of training data

Answer

One gradient descent update step

Answer

A group of similar states in the environment

Question 4

How does reinforcement learning fundamentally differ from supervised learning?

Accepted Answer

RL learns from environment interaction and reward signals, not labeled examples

Answer

RL uses labeled data while supervised learning uses rewards

Answer

RL cannot be applied to classification tasks

Answer

RL always requires significantly more data than supervised learning

Question 5

What is a policy in reinforcement learning?

Accepted Answer

A mapping from states to actions (or action probabilities) that defines agent behavior

Answer

The reward function provided by the environment

Answer

The transition probabilities between environment states

Answer

The maximum total reward achievable in an episode

Question 6

What does the state-value function V(s) represent in RL?

Accepted Answer

The expected cumulative discounted reward starting from state s under the current policy

Answer

The immediate reward received for entering state s

Answer

The number of times state s has been visited during training

Answer

The probability of transitioning out of state s

Question 7

What is the exploration-exploitation tradeoff in reinforcement learning?

Accepted Answer

Choosing between trying new actions to discover better strategies versus using known good actions

Answer

Balancing training speed against final model accuracy

Answer

Deciding between model-based and model-free RL approaches

Answer

Trading off reward magnitude versus frequency of reward