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

An RL agent learns by interacting with an environment to maximize the total cumulative reward it receives over time.

Question 2

What does the reward signal represent in reinforcement learning?

Accepted Answer

Immediate scalar feedback indicating how good an action was

Answer

The reward signal provides immediate scalar feedback to the agent, indicating the desirability of the action taken in the current state.

Question 3

In reinforcement learning, what is an 'episode'?

Accepted Answer

A complete sequence from the initial state to a terminal state

Answer

An episode is a complete sequence of interactions from the starting state to a terminal or goal state 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

Unlike supervised learning which uses labeled input-output pairs, RL agents learn from interactions with an environment using reward signals as feedback.

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

A policy defines the agent's behavior by mapping states to actions or probabilities of actions, determining what the agent does in each situation.

Machine Learning Practice Test

Machine Learning Practice Test

Machine Learning Reinforcement Learning