September 2024
Beginner to intermediate
364 pages
8h 27m
Korean
Content preview from 머신러닝 인터뷰 실무 가이드
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159
CHAPTER 03
기술 인터뷰: 머신러닝 알고리즘
TIP
대표적인 비모델 기반
RL
알고리즘에는
Q
-러닝,
SARSA
(상태 - 행동 - 보상 - 상태 - 행동
state
-
action
-
reward
-
state
-
action
)
54
, 근거리 정책 최적화
proximal
policy
optimization
(
PPO
) (
https
://
oreil
.
ly
/
M
-
POc
)
55
등이 있습
니다.
모델 기반
RL
에서 에이전트는 상태 전이
state
transition
(행동에 의해 상태가 변경되는 것 )를 내포
하는 환경의 모델을 학습합니다. 에이전트는 이 모델을 사용하여 주어진 상태에서 취할 최선
의 행동에 대한 결정을 내립니다. 따라서 모델 기반
RL
은 환경에 대한 명시적인 지식이 필요
합니다. 예시로는 동적 계획법과 몬테카를로 트리 탐색
MCTS
,
Monte
Carlo
tree
search
(
https
://
oreil
.
ly
/
ZuF22
) 등이 있습니다.
56
값 기반의 강화 학습
vs
정책 기반 강화 학습 요약
값 기반
Value
-
based
RL
은 특정 상태에서 특정 행동을 선택할 때의 기대 누적 보상(즉, ‘값’ )을
추정하는 것에 기초합니다.
Q
-러닝
57
,
SARSA
58
, 그리고
DQN
deep
Q
-
networks
이 값 기반
RL
에
해당합니다. 이 알고리즘들은 ...
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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.