Machine Learning Basics
Which of the following describes reinforcement learning?
- AGiving a large number of input–answer pairs and learning a function that predicts the answer from the input, so that the difference between prediction and answer becomes small
- BAn agent acts in an environment and learns a way of choosing actions (a policy) that makes the total reward it obtains large
- CDividing data without correct answers into several groups so that items with similar features end up in the same group
Show answer and explanation
Answer: B. An agent acts in an environment and learns a way of choosing actions (a policy) that makes the total reward it obtains large
In reinforcement learning, the learner (the agent) observes the state of the environment, chooses an action and receives a reward as a result. No correct answer is given for each action; through trial and error the agent learns a policy that maximizes the total reward received over the future. It is used for games such as Go, robot control and tuning the output of language models to match human evaluations.
- A. This describes supervised learning. In reinforcement learning, no correct answer is given for each action.
- B. Correct. The policy is learned by trial and error, guided by rewards.
- C. This describes clustering, a form of unsupervised learning.