September 2024
Beginner to intermediate
364 pages
8h 27m
Korean
Content preview from 머신러닝 인터뷰 실무 가이드
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158
머신러닝 인터뷰 실무 가이드
출구
로봇
폭탄
그림
3-14
강화 학습의 예: 미로를 탐색하는 로봇
이제 기대 누적 보상의 개념을 특정 상태에서 행동을 취했을 때의 기대 누적 보상인
Q
-값과
연결해 보겠습니다.
Q
-함수는 이와 관련된 개념으로써, 상태-행동 쌍을 입력 받아
Q
-값을
출력합니다. 정책은
RL
에이전트가 주어진 상태에서 취해야 할 행동을 결정합니다. 모든 것
을 하나로 묶어,
Q
-러닝에서 최적의 정책은 각 상태에서 가장 높은
Q
-값을 가진 행동을 선
택하는 것으로부터 나옵니다. 각 단계 후에는 정책이 평가되고 벨만 방정식
Bellman
equation
53
을
사용한 최적화 방법을 통해
Q
-값이 업데이트됩니다. 이 과정은 정책이 수렴하고 주어진 상
태에서 동일한 행동을 선택할 때까지 반복됩니다.
모델 기반 강화 학습
vs
비모델 강화 학습 요약
Q
-러닝은 세계, 즉 상태와 행동 간의 관계를 모델링하기 위한 모델을 사용하지 않기 때문에
비모델
model
-
free
RL
기법에 해당합니다. 상태와 행동을 나타내야 하지만,
Q
-값과 정책 (정책
반복을 사용할 경우 )을 개선하기 위해서는 보상을 관찰하기만 하면 됩니다.
53
“벨만 방정식,” 강화 학습 용어집,
2023
년
10
월
23
일 접속,
https
://
oreil
.
ly
/
KP8kh
.
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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.