Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to Machine Learning: Reinforcement Learnin

What is the primary difference between reinforcement learning and supervised learning?

• Supervised learning involves an agent and environment.

• Reinforcement learning uses labeled data.

• Reinforcement learning is about learning from experience.

• Supervised learning maximizes rewards.

In reinforcement learning, what does the agent aim to maximize?

• The amount of data collected

• The number of actions taken

• The number of states visited

• The total reward received

What is a characteristic of non-stationary environments in reinforcement learning?

• The states never change.

• The rewards are always the same for the same action.

• The agent does not receive any rewards.

• The rewards can vary for the same action.

How can reinforcement learning be related to supervised learning?

• By collecting data from multiple interactions with the environment

• By using a fixed set of rules

• By using labeled data from the start

• By avoiding any interaction with the environment

Which of the following is NOT an application of reinforcement learning?

• Traditional database management

• Recommender systems

• Autonomous cars

• Automatic game playing