September 2024
Beginner to intermediate
364 pages
8h 27m
Korean
Content preview from 머신러닝 인터뷰 실무 가이드
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154
머신러닝 인터뷰 실무 가이드
강화 학습 에이전트 요약
RL
에서 에이전트
Agent
는 특정 목표나 목적을 가지고 환경과 상호작용하는 자율적인 개체로
써, 시행착오를 통해 최적의 결정을 내리는 방법을 학습합니다. 예를 들어, 처음엔 운전에 미
숙했던 자율 주행 차가 테스트 환경에서 학습하면서 시간이 지남에 따라 어떤 행동이 좋은지
(속도 제한 및 도로 표지판 따르기 )와 나쁜지 (나무에 부딪히거나 빨간 신호에서 달리기 같
은)를 배워 나갑니다.
이전 절에서 언급된 대부분의 머신러닝 알고리즘에서는 모델이 주로 업데이트 되는 반면,
RL
에서는 에이전트가 환경과 상호작용하면서 업데이트됩니다. 이것은
RL
에 ‘모델’이 없다는 의
미는 아니지만, 모델들은 종종 전체
RL
워크플로 내에서 조합하고 결합할 수 있는 보조적인
구성 요소로 사용됩니다.
RL
에 대한 이해를 돕기 위해 단순화된 자율 주행 차의 예를 계속 들어보겠습니다. 기본적인
RL
에이전트를 만드는 데 필요한 구성 요소는 상태, 행동, 보상, 정책입니다.
TIP
강화 학습에는 여러 유형이 있고, 각 알고리즘에 따라 정책, 상태, 행동, 보상이 상호 작용하는 방식이 다
를 수 있으며 다른 개념과 조합/혼합하여 사용될 수도 있습니다. 인터뷰에서 어떤 개념에 대해 질문을 받고
있는지에 주의를 기울여야 합니다.
해당
RL ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.