Reinforcement learning from human feedback
id:
reinforcement-learning-from-human-feedback-204-14991577
title:
Reinforcement learning from human feedback
text:
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning. In classical reinforcement learning, an intelligent agent's goal is to learn a function that guides its behavior, called a policy. This function is iteratively updated to maximize rewards based on the agent's task perform
brand slug:
wiki
category slug:
encyclopedia
description:
Machine learning technique
original url:
https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback
date created:
2023-03-04T01:18:12Z
date modified:
2024-09-10T05:29:40Z
main entity:
{"identifier":"Q115570683","url":"https://www.wikidata.org/entity/Q115570683"}
image:
{"content_url":"https://upload.wikimedia.org/wikipedia/commons/b/b2/RLHF_diagram.svg","width":512,"height":366}
fields total:
13
integrity:
16