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強化学習による制御システムの自動反例生成
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Yoriyuki Yamagata
December 14, 2018
Technology
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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
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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