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
46
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
180
reinforcement_learning_.pdf
wonseokjung
2
1.5k
원석이의 모두연에서 강화학습 보석되기
wonseokjung
0
390
NeuralIPS
wonseokjung
0
360
Introduction Deep Reinforcement Learning
wonseokjung
0
130
Deep reinforcemenet learning -2
wonseokjung
0
170
Deep Reinforcement Learning - Introduction
wonseokjung
1
610
How to become a datascientist ?
wonseokjung
2
2.3k
Review of Taylor series
wonseokjung
1
120
Other Decks in Science
See All in Science
教師なしテンソル分解に基づく、有糸分裂後の転写再活性化におけるヒストン修飾ブックマークとしての転写因子候補の抽出法
tagtag
0
120
機械学習を支える連続最適化
nearme_tech
PRO
1
150
ベイズのはなし
techmathproject
0
290
拡散モデルの概要 −§2. スコアベースモデルについて−
nearme_tech
PRO
0
570
インフラだけではない MLOps の話 @事例でわかるMLOps 機械学習の成果をスケールさせる処方箋 発売記念
icoxfog417
2
580
Analysis-Ready Cloud-Optimized Data for your community and the entire world with Pangeo-Forge
jbusecke
0
110
ICRA2024 速報
rpc
3
5.2k
山形とさくらんぼに関するレクチャー(YG-900)
07jp27
1
220
Snowflakeによる統合バイオインフォマティクス
ktatsuya
0
490
(論文読み)贈り物の交換による地位の競争と社会構造の変化 - 文化人類学への統計物理学的アプローチ -
__ymgc__
1
100
Science of Scienceおよび科学計量学に関する研究論文の俯瞰可視化_ポスター版
hayataka88
0
130
General Parasitology
uni_of_nomi
0
120
Featured
See All Featured
Sharpening the Axe: The Primacy of Toolmaking
bcantrill
38
1.8k
Six Lessons from altMBA
skipperchong
27
3.5k
[RailsConf 2023 Opening Keynote] The Magic of Rails
eileencodes
28
9.1k
What’s in a name? Adding method to the madness
productmarketing
PRO
22
3.1k
Making the Leap to Tech Lead
cromwellryan
133
8.9k
How to Create Impact in a Changing Tech Landscape [PerfNow 2023]
tammyeverts
47
2.1k
Save Time (by Creating Custom Rails Generators)
garrettdimon
PRO
27
840
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
44
6.8k
Keith and Marios Guide to Fast Websites
keithpitt
409
22k
Fantastic passwords and where to find them - at NoRuKo
philnash
50
2.9k
The Cult of Friendly URLs
andyhume
78
6k
Side Projects
sachag
452
42k
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