Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Metalearning shared Hierarchy
Search
Wonseok Jung
August 28, 2018
Science
0
47
Metalearning shared Hierarchy
Metalearning shared Hierarchy
논문 review
Wonseok Jung
August 28, 2018
Tweet
Share
More Decks by Wonseok Jung
See All by Wonseok Jung
Ai for business -self car driving
wonseokjung
0
200
reinforcement_learning_.pdf
wonseokjung
2
1.5k
원석이의 모두연에서 강화학습 보석되기
wonseokjung
0
410
NeuralIPS
wonseokjung
0
410
Introduction Deep Reinforcement Learning
wonseokjung
0
150
Deep reinforcemenet learning -2
wonseokjung
0
190
Deep Reinforcement Learning - Introduction
wonseokjung
1
630
How to become a datascientist ?
wonseokjung
2
2.3k
Review of Taylor series
wonseokjung
1
120
Other Decks in Science
See All in Science
ランサムウェア対策にも考慮したVMware、Hyper-V、Azure、AWS間のリアルタイムレプリケーション「Zerto」を徹底解説
climbteam
0
100
「美は世界を救う」を心理学で実証したい~クラファンを通じた新しい研究方法
jimpe_hitsuwari
1
140
Explanatory material
yuki1986
0
390
オンプレミス環境にKubernetesを構築する
koukimiura
0
320
01_篠原弘道_SIPガバニングボード座長_ポスコロSIPへの期待.pdf
sip3ristex
0
630
実力評価性能を考慮した弓道高校生全国大会の大会制度設計の提案 / (konakalab presentation at MSS 2025.03)
konakalab
2
190
04_石井クンツ昌子_お茶の水女子大学理事_副学長_D_I社会実現へ向けて.pdf
sip3ristex
0
590
テンソル分解による糖尿病の組織特異的遺伝子発現の統合解析を用いた関連疾患の予測
tagtag
2
220
機械学習 - 決定木からはじめる機械学習
trycycle
PRO
0
1k
Ignite の1年間の軌跡
ktombow
0
140
Lean4による汎化誤差評価の形式化
milano0017
1
290
凸最適化からDC最適化まで
santana_hammer
1
280
Featured
See All Featured
Design and Strategy: How to Deal with People Who Don’t "Get" Design
morganepeng
131
19k
Designing for humans not robots
tammielis
253
25k
Visualizing Your Data: Incorporating Mongo into Loggly Infrastructure
mongodb
48
9.6k
The Straight Up "How To Draw Better" Workshop
denniskardys
236
140k
Building Better People: How to give real-time feedback that sticks.
wjessup
367
19k
Become a Pro
speakerdeck
PRO
29
5.5k
Designing for Performance
lara
610
69k
Improving Core Web Vitals using Speculation Rules API
sergeychernyshev
18
1.1k
Building Flexible Design Systems
yeseniaperezcruz
328
39k
Why Our Code Smells
bkeepers
PRO
338
57k
KATA
mclloyd
32
14k
Intergalactic Javascript Robots from Outer Space
tanoku
272
27k
Transcript
.FUB-FBOJOHTIBSFE)JFSBSDIZ 8POTFPL+VOH 3FJOGPSDFNFOU-FBSOJOH
ਗࢳ 8POTFPL+VOH $JUZ6OJWFSTJUZPG/FX:PSL#BSVDI$PMMFHF %BUB4DJFODF.BKPS $POOFYJPO"*"*3FTFBSDIFS %FFQ-FBSOJOH$PMMFHF3FJOGPSDFNFOU-FBSOJOH3FTFBSDIFS .PEVMBCT$53--FBEFS 3FJOGPSDFNFOU-FBSOJOH 0CKFDU%FUFDUJPO
$IBUCPU (JUIVC IUUQTHJUIVCDPNXPOTFPLKVOH 'BDFCPPL IUUQTXXXGBDFCPPLDPNXTKVOH #MPH IUUQTXPOTFPLKVOHHJUIVCJP
ݾର 1. Introduction 2. Problem Statement 3. Algorithm 4. Experiments
META LEARNING SHARED HIERARCHIES
1.INTRODUCTION
1. UTILIZE PRIOR KNOWLEDGE META LEARNING SHARED HIERARCHIES 6UJMJ[FQSJPSLOPXMFEHF .BTUFSOFXUBTL
1.1 BUT REINFORCEMENT… META LEARNING SHARED HIERARCHIES How about Reinforcement
Learning?
1.2 SOLVE EACH TASK INDEPENDENTLY AND FROM SCRATCH SUPERMARIO WITH
R.L https://www.youtube.com/watch?v=IjvbhwuCaF0
1.3 ISSUES META LEARNING SHARED HIERARCHIES Sharing information Task1 Task2
Task3 θ1 θ2 θ3
1.4 MASTER POLICY META LEARNING SHARED HIERARCHIES Master Policy Sub1
Sub2 Sub3 θ1 θ2 θ3
1.5 MLSH META LEARNING SHARED HIERARCHIES Metalearning shared hierarchies
2.PROBLEM STATEMENT
2.1 NOTATION Time step Action Transition Function Reward Set of
states Set of actions Start state Discount factor t a P(s′, r ∣ s, a) r A S S0 γ Set of reward Policy Reward State R π r REINFORCEMENT LEARNING s
2.2 NOTATION META LEARNING SHARED HIERARCHIES EJTUSJCVUJPOPWFS.%1T "HFOUחQBSBNFUFSWFDUPSܳӝਵ۽VQEBUFೠ పझٜՙܻҕਬೞחۄఠ
пపझۄఠ BHFOUоഅపझ.ਸߓݴসؘೞחۄఠ PM πθ,ϕ(a∣s) ϕ θ
"DUJPO "HFOU &OWJSPONFOU 3FXBSE At Rt 4UBUF St Rt+1 St+1
REINFORCEMENT LEARNING 2.3 OBJECTIVE MDP
REINFORCEMENT LEARNING 2.4 NEW MDP &OWJSPONFOU 3FXBSE At Rt St
Rt+1 St+1 5BQUIFCBMM 1PTJUJWF3FXBSE New MDP
SUPERMARIO WITH R.L 2.5 NEW MDP-2 "DUJPO "HFOU &OWJSPONFOU 3FXBSE
At Rt 4UBUF St Rt+1 St+1 3FXBSE 1FOBMUZ Another New MDP
2.6 FIND SHARING PARAMETER META LEARNING SHARED HIERARCHIES maximizeϕ EM∼PM
, t = 0...T − 1[R]
2.7 STRUCTURE META LEARNING SHARED HIERARCHIES
3.ALGORITHM
3.1 MLSH ALGORITHM META LEARNING SHARED HIERARCHIES
3.2 MLSH ALGORITHM META LEARNING SHARED HIERARCHIES Two main components
3.3 MLSH ALGORITHM META LEARNING SHARED HIERARCHIES Joint update period
Warmup period
3.4 MLSH ALGORITHM META LEARNING SHARED HIERARCHIES Joint update period
Warmup period
3.5 MLSH ALGORITHM META LEARNING SHARED HIERARCHIES Joint update period
Warmup period θ θ, ϕ update
3.6 MLSH ALGORITHM-2 META LEARNING SHARED HIERARCHIES Joint update period
Warmup period θ θ, ϕ update
3.7 MLSH ALGORITHM-WARMUP META LEARNING SHARED HIERARCHIES update
3.8 MLSH ALGORITHM- JOINT UPDATE PERIOD META LEARNING SHARED HIERARCHIES
update
3.8 MLSH ALGORITHM META LEARNING SHARED HIERARCHIES update
4. EXPERIMENTS
4.1 2D MOVING BANDITS TASK META LEARNING SHARED HIERARCHIES
4.2 RESULT(2D BALL) META LEARNING SHARED HIERARCHIES
4.3 WALKING, CRAWLING META LEARNING SHARED HIERARCHIES
4.4 WALKING, CRAWLING META LEARNING SHARED HIERARCHIES