Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
強化学習による制御システムの自動反例生成
Search
Yoriyuki Yamagata
December 14, 2018
Technology
3k
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
強化学習による制御システムの自動反例生成
第1回AI4SEセミナー講演資料
Yoriyuki Yamagata
December 14, 2018
More Decks by Yoriyuki Yamagata
See All by Yoriyuki Yamagata
Individual-based epidemiological model of COVID19 using location data
yoriyukiprf
0
150
On proving consistency of equational theories in bounded arithmetic
yoriyukiprf
0
220
On proving consistency of equational theories in bounded arithmetic
yoriyukiprf
0
190
個人レベルの位置情報を使ったCOVID19の感染モデル
yoriyukiprf
0
140
人流データを用いた人流制限解除後のCOVID-19感染状況の推定
yoriyukiprf
0
190
Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning
yoriyukiprf
0
220
引用の記述説の擁護
yoriyukiprf
0
270
Consistency proof of fragments of equational systems with substitution in bounded arithmetic
yoriyukiprf
0
180
Concepts on AI Fairness
yoriyukiprf
0
200
Other Decks in Technology
See All in Technology
Snowflakeのコスト最適化を支えるアーキテクチャ設計
ktatsuya
1
1.6k
AI 駆動 Terraform 開発/SRE_BizReach_MIXI_2
visional_engineering_and_design
5
2.1k
20260912_スクフェス三河
kgnkhkr
0
350
Sigmaで作る業務アプリ
kazushiro_honma
0
140
2026-09-11 【Snowflake World Tour Tokyo 2026】Snowflakeを起点に、AI Agentが自律稼働し続ける未来へ / Driving AI Agents with Snowflake
civitaspo
0
390
AI時代、データエンジニアが一番おもろい
genshun9
0
540
例外の正しい扱い方 そのエラー try-catchして大丈夫?
jinwatanabe
3
490
AIで仕事のやり方を変える
matsu7874
3
1.1k
What the customer really needed
kawaguti
PRO
1
110
フルカイテン株式会社 エンジニア向け採用資料
fullkaiten
0
12k
OpenTelemetry eBPF Instrumentationの舞台裏 / Behind the Scenes of OpenTelemetry eBPF Instrumentation
ymotongpoo
4
1.2k
2026/09/10 Spring Bootから Jakarta EE/MicroProfileへの移行
megascus
0
360
Featured
See All Featured
Ruling the World: When Life Gets Gamed
codingconduct
0
330
Creating an realtime collaboration tool: Agile Flush - .NET Oxford
marcduiker
35
2.6k
Bioeconomy Workshop: Dr. Julius Ecuru, Opportunities for a Bioeconomy in West Africa
akademiya2063
PRO
1
350
Site-Speed That Sticks
csswizardry
13
1.5k
16th Malabo Montpellier Forum Presentation
akademiya2063
PRO
0
380
The Curious Case for Waylosing
cassininazir
1
500
Odyssey Design
rkendrick25
PRO
2
800
Java REST API Framework Comparison - PWX 2021
mraible
34
9.7k
The browser strikes back
jonoalderson
0
1.7k
Avoiding the “Bad Training, Faster” Trap in the Age of AI
tmiket
0
240
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
49
3.6k
The AI Search Optimization Roadmap by Aleyda Solis
aleyda
1
6.2k
Transcript
ڧԽֶशʹΑΔ੍ޚγεςϜͷࣗಈྫੜ ࢁܗ↳೭ʢ࢈ۀٕज़૯߹ݚڀॴʣ ࡚ະʢ࢜௨ݚڀॴʣཱུɺஈၬւɺ㭟ݐြʢఱେֶʣ
3ߦ·ͱΊ • ੍ޚܥ͕ʮ͓͔͠ͳৼΔ͍ʯΛ͢ΔೖྗΛڧԽֶश Λͬͯࣗಈੜͨ͠ • ڧԽֶशΛΘͳ͍ख๏ʹൺͯɺޮతʹ୳ࡧͰ͖ Δʢ͍͍ͩͨͷ߹ʣ • ·࣮ͩ༻ʹఔԕ͍ʢͱࢥ͏ʣ
ڧԽֶश ΤʔδΣϯτ ڥ ΞΫγϣϯ རಘ ঢ়ଶ ؍ଌ
ڧԽֶश • ΤʔδΣϯτརಘͷظׂҾ͖ݱࡏՁΛ࠷େԽ͢ Δ • རಘͷকདྷʹͬͯͷ߹ܭͷظͱߟ͑ͯྑ͍ Ri = [ ∞
∑ k=i γk−irk] , γ ≤ 1 : constant
ڧԽֶशͷख๏ • Q-functionΛ༻͍ͨํ๏ʢDQNͳͲʣ • Q-functionʢঢ়ଶͱΞΫγϣϯ͔ΒظརಘΛٻΊΔ ؔʣΛਪఆ͢Δ • Actor-CriticʢA3CͳͲʣ • ʮΞΫλʔʯ͕ै͏ϙϦγʔΛɺͦͷύϑΥʔϚϯ
εΛਪఆ͢ΔʮΫϦςΟοΫʯ͕Ξοϓσʔτͯ͠ ͍͘
੍ޚγεςϜͷྫɿࣗಈมػ ΞΫηϧ ϒϨʔΩ Τϯδϯ ΪΞ
ࣗಈྫੜ ཁٻɿΤϯδϯຖ4770ճҎԼ ཁٻΛຬͨ͞ͳ͍ΞΫηϧɾϒϨʔΩύλʔϯ ΛࣗಈͰੜ͢Δ ΞΫηϧ ϒϨʔΩ Τϯδϯ ΪΞ
࠷దԽʹΑΔࣗಈྫੜ Τϯδϯ Τϯδϯͷ࠷େΛͰ͖Δ্͚ͩ͛Εྑ͍ →࠷దԽٕ๏͕͑Δʂ ӡసૢ࡞ 0 5 10 15 20
25 30 0 50 100 150 0 100 200 300 400 500 600 0 5 10 15 20 25 30 1000 1500 2000 2500 3000 3500 4000 4500 5000
͜Ε·Ͱͷ࠷దԽʹΑΔࣗಈྫੜͷΈ Τϯδϯ ӡసૢ࡞ 0 5 10 15 20 25 30
0 50 100 150 0 100 200 300 400 500 600 0 5 10 15 20 25 30 1000 1500 2000 2500 3000 3500 4000 4500 5000 ࠷ߴΤϯδϯ ࠷దԽΞϧΰϦζϜ 1ϧʔϓ=1γϛϡϨʔγϣϯ
ڧԽֶशʹΑΔࣗಈྫੜ γεςϜঢ়ଶ ӡసૢ࡞ 0 5 10 15 20 25 30
0 50 100 150 0 100 200 300 400 500 600 0 5 10 15 20 25 30 1000 1500 2000 2500 3000 3500 4000 4500 5000 Τϯδϯ ڧԽֶश 1ϧʔϓ=γϛϡϨʔγϣϯ1εςοϓ
རಘͷઃܭ Ri = [ ∞ ∑ k=i γk−irk] , γ
≤ 1 : constant ڧԽֶशརಘͷΛ࠷େԽ͢Δ ྫੜΤϯδϯͷ࠷େΛ࠷େԽ͢Δ robi = T max k=i ωk , ωk : engine speed
རಘͷઃܭɿ࠷େͷʹΑΔۙࣅ max{x1 , …, xn } ∼ log n ∑
i=1 {exi − 1} Λ͏ robi = T max k=i ωk ∼ log T ∑ k=i {eωk − 1} Ri = [ ∞ ∑ k=i γk−irk] ͱݟൺΔͱ ri = eωi − 1 γ = 1 ͱஔ͚ྑ͍
ຊ͏গ͠ෳࡶ • ͬͱ͍Ζ͍Ζͳੑ࣭ͷྫੜΛ͍ͨ͠ • MTLͱ͍͏ཧࣜͰ͔͚Δੑ࣭ͷҰ෦͕ରԠՄ • རಘͷಋग़͕͏গ͠ෳࡶʹͳΔ • εέʔϦϯάʹΑΔਖ਼نԽ •
ೖग़ྗΛεέʔϦϯάͯ͠ൺֱతখ͍࣮͞ʹ͢Δ • ࣮ࡍͷ੍ޚܥ࿈ଓ࣌ؒͳͷͰ࣌ؒΛ۠Δඞཁ͕͋Δ
࣮ݧɿ࣮ • ੍ޚܥͷϞσϧ • Matlab/Simulinkʹ͍ͭͯ͘Δsldemo_autotrans • ڧԽֶश • ChainerRLͷA3C͓ΑͼDDQN+NAF •
ϋΠύʔύϥϝʔλʔνϡʔχϯά͍ͯ͠ͳ͍ • طଘख๏ • S-Taliroͷম͖ͳ·͠๏ʢSAʣ͓ΑͼCross Entropy๏ʢCEʣ
࣮ݧɿઃఆ • ֤ੑ࣭ɿφ1−φ9ʹ • ֤ΞϧΰϦζϜɿA3C, DDQN, SA, CEΛ • 100ηογϣϯಉ݅͡Ͱద༻
• 1ηογϣϯʹ͖ͭ࠷େ200ճγϛϡϨʔγϣϯ͕Մೳ • 1ηογϣϯ͕ऴΘΔͱֶश༰ফڈ͞ΕΔ
A3C-1 A3C-5 A3C-10 DQN-1 DQN-5 DQN-10 CE-1 CE-5 CE-10 SA-1
SA-5 SA-10 φ1 80 60 70 98 80 90 2 23 14 0 13 8 φ2 47 42 42 99 100 92 5 22 5 0 16 26 φ3 68 0 0 52 0 0 83 0 0 23 0 0 φ4 72 0 0 48 0 0 84 0 0 21 0 0 φ5 100 1 0 100 0 0 100 0 0 100 0 0 φ6 62 71 78 100 100 100 0 98 100 0 26 79 φ7 37 34 41 52 99 100 0 0 0 0 0 0 φ8 35 21 36 93 100 100 100 99 97 21 77 94 φ9 38 53 57 68 100 100 0 0 4 0 0 2 ྫੜͷޭ
0 50 100 150 200 A3C−1 A3C−5 A3C−10DDQN−1 DDQN−5 DDQN−10
CE−1 CE−5 CE−10 SA−1 SA−5 SA−10 Algorithm Number of Episodes fml1
0 50 100 150 200 A3C−1 A3C−5 A3C−10DDQN−1 DDQN−5 DDQN−10
CE−1 CE−5 CE−10 SA−1 SA−5 SA−10 Algorithm Number of Episodes fml2
0 50 100 150 200 A3C−1 A3C−5 A3C−10DDQN−1 DDQN−5 DDQN−10
CE−1 CE−5 CE−10 SA−1 SA−5 SA−10 Algorithm Number of Episodes fml3
0 50 100 150 200 A3C−1 A3C−5 A3C−10DDQN−1 DDQN−5 DDQN−10
CE−1 CE−5 CE−10 SA−1 SA−5 SA−10 Algorithm Number of Episodes fml4
0 50 100 150 200 A3C−1 A3C−5 A3C−10DDQN−1 DDQN−5 DDQN−10
CE−1 CE−5 CE−10 SA−1 SA−5 SA−10 Algorithm Number of Episodes fml5
0 50 100 150 200 A3C−1 A3C−5 A3C−10DDQN−1 DDQN−5 DDQN−10
CE−1 CE−5 CE−10 SA−1 SA−5 SA−10 Algorithm Number of Episodes fml6
0 50 100 150 200 A3C−1 A3C−5 A3C−10DDQN−1 DDQN−5 DDQN−10
CE−1 CE−5 CE−10 SA−1 SA−5 SA−10 Algorithm Number of Episodes fml7
0 50 100 150 200 A3C−1 A3C−5 A3C−10DDQN−1 DDQN−5 DDQN−10
CE−1 CE−5 CE−10 SA−1 SA−5 SA−10 Algorithm Number of Episodes fml8
0 50 100 150 200 A3C−1 A3C−5 A3C−10DDQN−1 DDQN−5 DDQN−10
CE−1 CE−5 CE−10 SA−1 SA−5 SA−10 Algorithm Number of Episodes fml9
ߟ • DDQNͲͷ՝ɾઃఆͰൺֱత҆ఆͯ͠ੑೳΛग़͠ ͍ͯΔ • ΪΞʹؔ͢Δੑ࣭ʹ͍ͭͯA3C, DDQNCEʹྼΔ • ͓ͦΒ͘ΪΞෆ࿈ଓʹมԽ͢ΔͨΊ •
ڧԽֶशγϛϡϨʔγϣϯճ͕ಉ͡ͳΒ ͍͕ɺ͜Ε࣮ͷ
3ߦ·ͱΊ • ੍ޚܥ͕ʮ͓͔͠ͳৼΔ͍ʯΛ͢ΔೖྗΛڧԽֶश Λͬͯࣗಈੜͨ͠ • ڧԽֶशΛΘͳ͍ख๏ʹൺͯɺޮతʹ୳ࡧͰ͖ Δʢ͍͍ͩͨͷ߹ʣ • ·࣮ͩ༻ʹఔԕ͍ʢͱࢥ͏ʣ