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What is Reinforcement learning?

A machine learning approach in which an agent (the learner) observes the state of an environment, chooses actions and learns by trial and error a way of choosing actions (a policy) that maximizes the total reward it receives, including future rewards. No correct answer is given for each action, and rewards often arrive with a delay. It is used for games, robot control and for tuning the output of language models to match human evaluations.

Japanese name: 強化学習

Reinforcement learning appears in 2 practice questions across 1 topic, including Machine Learning Basics.

Field: Machine Learning Basics

Try it: questions about Reinforcement learning

Which of the following describes reinforcement learning?

  1. 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
  2. BAn agent acts in an environment and learns a way of choosing actions (a policy) that makes the total reward it obtains large
  3. 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.

Reinforcement learning in the explanations

Questions that mention Reinforcement learning (2)

  1. Which of the following describes reinforcement learning?
  2. Which of the following is an example of unsupervised learning?

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Related terms

Unsupervised learning Supervised learning Activation function Confusion matrix Convolutional neural network (CNN) Cross-validation Gradient descent k-means clustering Overfitting Precision and recall

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