September 2024
Beginner to intermediate
364 pages
8h 27m
Korean
Content preview from 머신러닝 인터뷰 실무 가이드
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161
CHAPTER 03
기술 인터뷰: 머신러닝 알고리즘
트되는 정책 )이라면, 그 강화 학습은 온-폴리시
RL
입니다.
오프-폴리시
Off
-
Policy
강화 학습 알고리즘은 다른 정책 또는 결합된 정책으로부터 수집된 데이
터 포인트 또는 경험을 바탕으로 정책을 업데이트합니다.
Q
-러닝과
DQN
은 오프-폴리시
강화 학습에 해당합니다. 그런데 공교롭게도 이 알고리즘들은 값 기반 강화 학습이기도 하
죠. 혼란을 방지하기 위해 말씀드리면, 온-폴리시 대 오프-폴리시는 에이전트가 업데이트되
는 새로운 정책을 사용하는지 여부와 관련이 있으며, 정책 기반 대 값 기반은 최적의 행동을
도출하기 위해 사용된 알고리즘의 유형과 관련이 있습니다.
이 외에도 우리가 더 살펴볼 수 있는 알고리즘이 많이 있습니다. 시간차
Temporal
Difference
(
TD
),
비동기적 어드밴티지 액터 크리틱
Asynchronous
Advantage
Actor
-
Critic
(
A3C
),
PPO
등이 있습니다. 이
에 대해 더 알고 싶은 분들은 리처드 서튼과 앤드류 바토가 저술한 강화 학습 교과서를 온라
인(
https
://
oreil
.
ly
/
MCgBK
)또는 절 시작 앞 부분의 자료에서 확인해보세요.
강화 학습에 관한 인터뷰 문제 예시
이제 기초적인
RL
개념에 친숙해졌으니, 몇 가지 인터뷰 문제 예시를 살펴보겠습니다. ...
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and much more.
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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.