May 2020
Beginner to intermediate
952 pages
24h 11m
Korean
Content preview from 핸즈온 머신러닝(2판): 사이킷런, 케라스, 텐서플로 2를 활용한 머신러닝, 딥러닝 완벽 실무
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729
18
장
강화 학습
18.4
신경망 정책
신경망 정책을 만들어봅시다. 앞서 하드코딩한 정책과 마찬가지로 이 신경망은 관측을 입력으
로 받고 실행할 행동을 출력합니다. 조금 더 정확히 말해 각 행동에 대한 확률을 추정합니다.
그리고 추정된 확률에 따라 랜덤하게 행동을 선택할 것입니다 (그림
18
-
5
).
CartPole
환경의
경우엔 가능한 행동이 두 개(왼쪽과 오른쪽) 있으므로 하나의 출력 뉴런만 있으면 됩니다. 이
뉴런은 행동
0
(왼쪽)의 확률을 출력합니다. 당연하게 행동
1
(오른쪽)의 확률은
1
-
p
가 됩니
다. 예를 들어
0
.
7
을 출력했다면 행동
0
은
70
% 확률로 선택될 것이고, 행동
1
은
30
% 확률로
선택될 것입니다.
다항 분포 샘플링
행동
관측
은닉층
행동 0(왼쪽)의 확률
그림
18-5
신경망 정책
왜 가장 높은 점수의 행동을 그냥 선택하지 않고 신경망이 만든 확률을 기반으로 랜덤하게 행
동을 선택하는지 궁금할 것입니다. 이런 방식은 에이전트가 새로운 행동을
탐험
exploring
하는 것
과 잘 할 수 있는 행동을
활용
exploiting
하는 것 사이에 균형을 맞추게 합니다. 비유를 들어볼까
요? 어떤 음식점에 처음 방문했다고 합시다. 모든 메뉴가 좋아 보여서 아무거나 골랐습니다.
음식이 좋았다면 다음에 다시 같은 메뉴를 주문할 가능성이 높을 것입니다. 하지만 ...
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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.