game dev light

Transcript

1. 1.

   ͜Ε͔ΒͷϏσΦήʔϜ࢈ۀʹ͍ͭͯ ը૾ͳ͠൛

2. 2.

   ೥ޙΛݟਾ͑Δ ͜Ε͔ΒͷϏσΦήʔϜ࢈ۀʹ͍ͭͯ

3. 3.

   IUUQTXXXVOSFBMFOHJOFDPNKBCMPHXFMDPNFUPVOSFBMFOHJOF GGXΛඪ४γΣʔμͱ͢ΔUnreal Engine 4͕ϦϦʔε͞ΕΔ 2014೥ Unity͕ඪ४γΣʔμΛGGXʹมߋ IUUQTVOJUZEDPNKQVOJUZXIBUTOFXVOJUZ 2015೥

4. 4.

   IUUQTXXXVOSFBMFOHJOFDPNKBCMPHXFMDPNFUPVOSFBMFOHJOF GGXΛඪ४γΣʔμͱ͢ΔUnreal Engine 4͕ϦϦʔε͞ΕΔ ࣍ʹϏσΦήʔϜ࢈ۀʹ޿·Δٕज़͸ಡΊΔ GGXʹؔ͢Δ࠷ॳͷ࿦จ Bruce Walter, Stephen R.

   Marschner, Hongsong Li, and Kenneth E. Torrance. 2007. Microfacet models for refraction through rough surfaces. In Proceedings of the 18th Eurographics conference on Rendering Techniques (EGSR'07), Jan Kautz and Sumanta Pattanaik (Eds.). Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 195-206. DOI=http:// dx.doi.org/10.2312/EGWR/EGSR07/195-206 2007೥ Stephen McAuley, Stephen Hill, Naty Hoffman, Yoshiharu Gotanda, Brian Smits, Brent Burley, and Adam Martinez. 2012. Practical physically-based shading in film and game production. In ACM SIGGRAPH 2012 Courses (SIGGRAPH '12). ACM, New York, NY, USA, Article 10, 7 pages. DOI: https://doi.org/10.1145/2343483.2343493 σΟεχʔ͕GGXΛ࢖͍ͬͯΔࣄΛެද 2012೥ 2014೥ Unity͕ඪ४γΣʔμΛGGXʹมߋ IUUQTVOJUZEDPNKQVOJUZXIBUTOFXVOJUZ 2015೥
5. 5.

   ࠓݚڀ͕੝ΜʹߦΘΕ͍ͯͯ
   ਺೥Ҏ಺ʹήʔϜ࢈ۀͰԠ༻͕޿͕Γͦ͏ͳٕज़͸ ػցֶश Machine Learning ಛʹήʔϜ԰ͷ࢓ࣄΛେ͖͘ม͑ͦ͏ͳͷ͕
   ఢରతੜ੒ωοτϫʔΫͱਂ૚2ωοτϫʔΫ

6. 6.

   https://www.reddit.com/r/zelda/comments/5olf6h/ oc_i_fixed_my_zelda_map_size_comparison_graphic/ θϧμͷ఻આ
   ࣌ͷΦΧϦφ  θϧμͷ఻આ
   ෩ͷλΫτ  θϧμͷ఻આ
   τϫΠϥΠτϓϦϯηε  5IF
   &MEFS
   4DSPMMT
   7
   

   4LZSJN  θϧμͷ఻આ
   ϒϨεΦϒβϫΠϧυ 
7. 7.

   https://www.reddit.com/r/gaming/comments/5p6db2/ zelda_breath_of_the_wild_fair_map_comparison/ 5IF
   8JUDIFS
   
   8JME
   )VOU  (SBOE
   5IFGU
   "VUP
   7  5IF
   &MEFS
   4DSPMMT
   7
   4LZSJN  θϧμͷ఻આ
   

   ϒϨεΦϒβϫΠϧυ 
8. 8.

   ͭͷήʔϜΛ࡞ΔͨΊʹ
   ࡞Βͳ͚Ε͹ͳΒͳ͍෺͸૿͍͑ͯΔ ͭͷήʔϜΛ࡞ΔͨΊʹ
   ࢖͏͜ͱ͕Ͱ͖ΔϦιʔε͸༗ݶͰ͋Δ ͍͔ʹޮ཰Α͘ήʔϜΛ࡞Δ͔͕
   ॏཁʹͳ͍ͬͯΔ

9. 9.

   Coins $ - 7 gold pieces ---------- ------------ Armor #-........|

   ##...........+####### ---------- a - an uncursed +2 pair of leather gloves (being worn) |.........#####|.....)....| #####|........| b - an uncursed +1 robe (being worn) .........| ####...........-# #|........| Comestibles |........| # ------------# #|<.......| g - 3 uncursed food rations |........| # # #-........- h - 6 uncursed apples ----------## # #|........| i - 8 uncursed oranges #### # #---------- j - 4 uncursed fortune cookies # # ########### l - a food ration # # # # m - 2 jackal corpses ####### # # p - a kobold corpse ## ## ## Scrolls -------# # # d - an uncursed scroll of identify |.f.@.-# # ## n - a scroll labeled ZLORFIK |...>.|# #------------# Spellbooks |.....| #|..........|# c - a blessed spellbook of protection |...... #...........-# Potions |.....| |..........| e - 2 uncursed potions of healing ------- --------|--- f - a blessed potion of healing # Tools Foo the Candidate St:15 Dx:14 Co:12 In:9 Wi:1 k - a magic marker (0:43) Dlvl:1 $:7 HP:14(14) Pw:5(5) AC:4 Xp:1 Stressed Gems/Stones o - 6 rocks (end) େ͖ͳ෺Λখ͘͞࡞Δʹ͸ ࣗಈੜ੒ ೥୅ॳ಄ʹ͸طʹ
   μϯδϣϯΛࣗಈੜ੒͢ΔήʔϜ͕࡞ΒΕ͍ͯͨ
10. 10.

    ۙ୅తͳ%ήʔϜͰ
    ࣗಈੜ੒Λߦ͓͏ͱ͢ΔͱͲ͏ͳΔ͔

11. 11.

    https://www.gdcvault.com/play/1020197/Landscape-Creation-and-Rendering-in ۙ୅తͳ%ήʔϜͰ
    ࣗಈੜ੒Λߦ͓͏ͱ͢ΔͱͲ͏ͳΔ͔ ஍ܗΛਓ͕ؒ༻ҙͯ͠
    ຊ෺Β͍͠ςΫενϟͱ
    ຊ෺Β͍͠৭ͱ഑ஔͷ૲໦Λੜ੒͢Δ ͜Ε͚ͩͰ΋
    ෳࡶͳϧʔϧ
    Λཁ͢Δ

12. 12.

    https://minecraft.net/ja-jp/ ࢥ͍੾ͬͯࣗಈੜ੒͠΍͘͢ͳΔ੍໿ͷ΋ͱͰ஍ܗΛ࡞Δ ݡ͍બ୒Ͱ͸͋Δ͕
    ൚༻తʹ࢖͑Δํ๏Ͱ͸ͳ͍ ޿େͳੈքΛ
    খ͘͞࡞Δ͜ͱʹ
    ੒ޭ͍ͯ͠Δ

13. 13.

    େ͖ͳ෺Λখ͘͞࡞Δʹ͸
    ࣗಈੜ੒
    ͕༗ޮ͕ͩ ΠϚυΩͷήʔϜͷίϯςϯπΛ
    ࣗಈੜ੒͢ΔϧʔϧΛߟ͑Δͷ͸༰қͰ͸ͳ͍

14. 14.

    ? ԿΒ͔ͷϧʔϧ ͜͏͍͏࣌ ͜͏ͳͬͯཉ͍͠ ࣗಈੜ੒͸ԿͰ΋ϥϯμϜʹੜ੒͢Ε͹ྑ͍Θ͚Ͱ͸ͳ͘
    ଟ͘ͷ৔߹͜Μͳ෩ʹੜ੒͍ͨ͠ͱ͍͏໨ඪ͕͋Δ

15. 15.

    ? ԿΒ͔ͷϧʔϧ ͜͏͍͏࣌ ͜͏ͳͬͯཉ͍͠ ط஌ ط஌ ະ஌ ͍͔ͭ͘ͷࣄྫ͔Β
    ͦͷഎޙʹ͋Δϧʔϧ͕ԿͰ͋Δ͔Λߟ͑Δ໰୊ʹͳΔ ࣗಈੜ੒͸ԿͰ΋ϥϯμϜʹੜ੒͢Ε͹ྑ͍Θ͚Ͱ͸ͳ͘
    

    ଟ͘ͷ৔߹͜Μͳ෩ʹੜ੒͍ͨ͠ͱ͍͏໨ඪ͕͋Δ
16. 16.

    ػցֶश ? ͜ΕΛਓ͕ؒߟ͑ΔͷͰ͸ͳ͘
    ܭࢉػΛ࢖ͬͯٻΊΑ͏ͱ͍͏ͷ͕ ೖྗ ग़ྗ ͍͔ͭ͘ͷࣄྫ͔Β
    ͦͷഎޙʹ͋Δϧʔϧ͕ԿͰ͋Δ͔Λߟ͑Δ໰୊ʹͳΔ Machine Learning

17. 17.

    ڱ͍ ޿͍ ఢ͕ଟ͍ ఢ͕গͳ͍ ෦԰ͷಛ௃ ͷ͓खຊ͸͜͜ͰๅശΛग़͢΂͖ͩͱݴ͍ͬͯΔ ͷ͓खຊ͸͜͜ͰๅശΛग़͢ͷ͸ૣ͍ͱݴ͍ͬͯΔ ͷ৚݅Ͱ࡞ΒΕͨ෦԰ʹๅശ͸ग़Δ΂͖͔ ๅശग़ͤ೿ ๅശ·ͩ೿

18. 18.

    ڱ͍ ޿͍ ఢ͕ଟ͍ ఢ͕গͳ͍ ෦԰ͷಛ௃ ͳΜͱͳ͘͜ͷลͰ෼͔Εͯͦ͏͔ͩΒๅശΛग़ͦ͏ ͷ৚݅Ͱ࡞ΒΕͨ෦԰ʹๅശ͸ग़Δ΂͖͔ ๅശग़ͤ೿ ๅശ·ͩ೿

19. 19.

    ڱ͍ ޿͍ ఢ͕ଟ͍ ఢ͕গͳ͍ ෦԰ͷಛ௃ αϙʔτϕΫλϚγϯ ૬൓͢Δछྨͷ͓खຊ͔Β࠷େͷڑ཭ΛऔΕΔͱ͜ΖͰઢҾ͖ Support Vector Machine

20. 20.

    ڱ͍ ޿͍ ఢ͕ଟ͍ ఢ͕গͳ͍ ͬͪͩͬͨ͜Β
    ๅശ͕ग़Δ ͬͪͩͬͨ͜Β
    ๅശ͕ग़ͳ͍ ෦԰ͷಛ௃ σʔλ͔Βϧʔϧ͕Ͱ͖ͨ

21. 21.

    ڱ͍ ޿͍ ఢ͕ଟ͍ ఢ͕গͳ͍ ෦԰ͷಛ௃ ৚͕݅ෳࡶʹͳΔͱ
    ͜ͷํ๏ͰϧʔϧΛੜΈग़͢ͷ͸ແཧͦ͏

22. 22.

    ڱ͍ ޿͍ ఢ͕ଟ͍ ఢ͕গͳ͍ ෦԰ͷಛ௃ ͲΜͳઢͰ෼ྨͰ͖Δ͔΋෼͔Βͳ͍ॴ͔Β
    ϧʔϧΛಘ͍ͨ

23. 23.

    ৚݅෇͖ఢରతੜ੒ωοτϫʔΫΛ༻͍ͨ
    ຊ෺Β͍͠஍ܗͷੜ੒ Éric Guérin, Julie Digne, Éric Galin, Adrien Peytavie,

    Christian Wolf, Bedrich Benes, and Benoît Martinez. 2017. Interactive example-based terrain authoring with conditional generative adversarial networks. ACM Trans. Graph. 36, 6, Article 228 (November 2017), 13 pages. DOI: https://doi.org/10.1145/3130800.3130804 ෼Ͱඳ͔Εͨεέον͔Βੜ੒͞Εͨ஍ܗ
24. 24.

    ೔ຊޠͰཔΉ ৚݅෇͖ఢରతੜ੒ωοτϫʔΫΛ༻͍ͨ
    ຊ෺Β͍͠஍ܗͷੜ੒

25. 25.

    χϡʔϥϧωοτϫʔΫ ೖྗ ग़ྗ Neural Network ৚݅෇͖ఢରతੜ੒ωοτϫʔΫΛ༻͍ͨ
    ຊ෺Β͍͠஍ܗͷੜ੒ ૚ ܗࣜχϡʔϩϯ

26. 26.

    ܗࣜχϡʔϩϯ ೖྗ1 ೖྗ2 ೖྗ3 ೖྗ4 ೖྗ5 w1 w2 w3 w4

    w5 શͯͷೖྗʹ͋ΔॏΈwΛ ͔͚ͨ΋ͷͷ࿨ΛٻΊͯ ͜ΕΛແ਺ʹܨ͛ͨ΋ͷ͸ ॏΈ࣍ୈͰ ೚ҙͷؔ਺ΛۙࣅͰ͖Δ ReLU ग़ྗ ׆ੑԽؔ਺(ReLU) ʹ௨ͨ͠஋Λग़ྗ Formal Neuron
27. 27.

    ͱΓ͋͑ͣద౰ͳॏΈΛઃఆͨ͠ωοτϫʔΫʹ
    ೖྗΛྲྀ͢ͱ ೖྗ ཉ͔ͬͨ͠ग़ྗͱ͸͍ͩͿҧ͏෺͕ग़ͯ͘Δ ≠ ཉ͔ͬͨ͠ग़ྗ ग़ྗ

28. 28.

    ग़ྗ͕ͲΕ͘Β͍ཉ͔ͬͨ͠෺ʹ͍͔ۙΛද͢஋Λఆٛ͢Δ ଛࣦؔ਺ ໨ඪ ݁ߏࣅͯΔ ͪΐͬͱࣅͯΔ ࣅͯͳ͍ Loss Function

29. 29.

    w0k w1k w2k w3k w4k yk x0 x1 x2 x3

    x4 yk = R (∑ n wnk xn) YͱX͕෼͔͍ͬͯͯZΛٻΊΔํ๏ ∂E ∂wjk = ∂E ∂xk R′ k (∑ n wnk xn) xj XͷมԽ͕ଛࣦؔ਺ʹ༩͑ΔӨڹXʹΑΔภඍ෼ R′ k͸3F-6ΛLʹ͍ͭͯඍ෼ͨ͠΋ͷ R͸׆ੑԽؔ਺3F-6
30. 30.

    ∂E ∂wij = ∑ k ∂E ∂xk R′ k (∑

    n wnk xn) wjk R′ j (∑ n wnj xn) xi i j k ࠷ޙ͔Β૚໨ͷภඍ෼͸
    ߹੒ؔ਺ͷඍ෼Λ࢖ͬͯٻΊΔ ઌ΄ͲٻΊͨ࠷ޙͷ૚ͷภඍ෼ͷ஋Λ࢖͑Δ ࿈࠯཯ Chain Rule
31. 31.

    ଛࣦؔ਺ͷ஋ ग़ޱଆ͔Βॱ൪ʹ
    XΛม͑Δͱग़ྗ͕Ͳ͏มΘΔ͔
    ΛٻΊ͍ͯ͘ ޡࠩٯ఻೻๏ Back Propagation

32. 32.

    ଛࣦؔ਺ͷ஋Λখ͍ͨ͘͞͠
    ଛࣦؔ਺͕ࢁͩͱ͢Δͱ
    Ұ൪௿͍ॴʹߦ͖͍ͨ
    ͨͩ͠ࢁશମͷܗ͸Θ͔Βͳ͍ Xʹ͍ͭͯͷภඍ෼͸
    ࠓཱ͍ͬͯΔ৔ॴͷ
    ܏ࣼ ܏ࣼʹԊͬͯ
    ΑΓ௿͍ํ΁Ҡಈ͍͚ͯ͠͹
    Ұ൪௿͍ͱ͜ΖʹͨͲΓண͚Δؾ͕͢Δ

    ֶश Training ภඍ෼ͷ݁ՌʹԊͬͯXΛগ͠ಈ͔ͤ͹
    ໨ඪͱ͢Δؔ਺ʹগۙͮ͘͠ؾ͕͢Δ
33. 33.

    ޡࠩٯ఻೻ͰXΛमਖ਼ ͜ΕΛେྔͷσʔλʹ͍ͭͯ܁Γฦ͢ͱ
    χϡʔϥϧωοτϫʔΫ͸
    ೖྗʹରͯ͠ظ଴͢Δग़ྗ͕ಘΒΕΔؔ਺ʹ͍͍ۙͮͯ͘ ೖྗ ଛࣦؔ਺ ग़ྗ σʔλ͔Βؔ਺͕Ͱ͖ͨ

34. 34.

    ∂E ∂wij = ∑ k ∂E ∂xk R′ k (∑

    n wnk xn) wjk R′ j (∑ n wnj xn) xi ஈḪΔຖʹ׆ੑԽؔ਺ͷඍ෼ֻ͕͔Δ
    ͜ͷ஋͕খ͍͞ͱ্ͷ૚·ͰXͷमਖ਼͕ಧ͔ͳ͍ ReLU ReLUͷඍ෼ ඍ෼ͯ͠΋஋͕খ͘͞ͳΒͳ͍
    ׆ੑԽؔ਺3F-6ͷൃݟʹΑΓ
    ௕͍ωοτϫʔΫ͕࣮༻తʹͳͬͨ σΟʔϓϥʔχϯά Deep Learning
35. 35.

    ը૾ͷΑ͏ͳҐஔؔ܎Λ࣋ͬͨσʔλͰ͸
    ࿈ଓͨ͠ϐΫηϧ͕Ͳ͏ฒΜͰ͍Δ͔͸
    ॏཁͳ৘ใͰ͋Δ ? ΋͔ͯ͠͠
    ͳ

36. 36.

    w13 w23 w33 w43 w53 y3 x1 x2 x3 x4

    x5 w93 ʜ ͷΑ͏ͳԕ͘ͷϐΫηϧͱͷ઀ଓ͸
    ແବʹͳΔՄೳੑ͕ߴ͍ w93
37. 37.

    ը૾ͷہॴతͳಛ௃͸
    ը૾ʹϑΟϧλΛద༻͢ΔࣄͰऔΓग़ͤΔ        

     × ྫ
    Τοδݕग़
38. 38.

    ϑΟϧλ ೖྗ ग़ྗ ֶश͢Δը૾ॲཧϑΟϧλ ৞ΈࠐΈ Convolution

39. 39.

    ͜Μͳ෩ʹܨ͍Ͱ
    χϡʔϩϯΛݮΒ͍ͯ͘͠ࣄͰ ը૾͔Βॏཁͳಛ௃͚ͩΛ
    औΓग़͢ࣄΛࢼΈΔ େྔͷೖྗ ݶΒΕͨग़ྗ ը૾ʹԿ͕ඳ͔Ε͍ͯΔ͔Λ
    ೝࣝ͢ΔΑ͏ͳ৔߹ʹΑ͘࢖͏ ৞ΈࠐΈ Convolution

40. 40.

    ٯ৞ΈࠐΈ େྔͷೖྗ େྔͷग़ྗ Ұ౓ॏཁͳಛ௃͚ͩΛऔΓग़͠
    ৄࡉͳը૾ͷ෮ݩΛࢼΈΔ ը૾͔Βผͷը૾Λ
    ੜ੒͢Δ৔߹ʹΑ͘࢖͏ Deconvolution

41. 41.

    ৞ΈࠐΈ
    χϡʔϥϧωοτϫʔΫ Convolutional Neural Network

42. 42.

    ਤʹ͢Δͷ͕ਏ͍ͷͰ
    Ҏ߱χϡʔϥϧωοτϫʔΫͷ૚Λ ͜ͷΑ͏ʹॻ͘

43. 43.

    ஍ܗͷੜ੒ ৞ΈࠐΈ ٯ৞ΈࠐΈ ϥϯμϜͳը૾Λྲྀ͢ͱ
    ຊ෺ͬΆ͍஍ܗͷߴ͞Ϛοϓʹͳͬͯ
    ग़͖ͯͯ΄͍͠

44. 44.

    ଛࣦؔ਺ ஍ܗͷຊ෺ͬΆ͞ΛධՁ͢Δؔ਺͕ඞཁ Θ͔ΒΜ ຊ෺ͬΆ͍஍ܗ࡞Δ

45. 45.

    ෱Ԭ Ѫ஌ ळా ਓ޻Ӵ੕ʹΑͬͯଌྔ͞Ε࣮ͨࡍͷ஍ܗσʔλ͸
    ൺֱత؆୯ʹखʹೖΕΔ͜ͱ͕Ͱ͖Δ

46. 46.

    ຊ෺ͬΆ͍஍ܗ࡞Δ ຊ෺ͷ஍ܗ ੜ੒ͨ͠஍ܗ ஍ܗ͕ຊ෺͔൑ผ͢Δ΋͠൑ผʹࣦഊͨ͠ͳΒ
    ੜ੒ͨ͠஍ܗ͸
    ຊ෺Β͍͠஍ܗͰ͋Δ ఢରతੜ੒ωοτϫʔΫ Generative Adversarial Network

    I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio. Generative adversarial nets. In Proceedings of NIPS, pages 2672– 2680, 2014. papers.nips.cc/paper/5423- generativeadversarial-nets.pdf.
47. 47.

    ఢରతੜ੒ωοτϫʔΫ Generative Adversarial Network ຊ෺ͬΆ͍஍ܗ࡞Δ ஍ܗ͕ຊ෺͔൑ผ͢Δ ຊ෺ͷ஍ܗ ੜ੒ͨ͠஍ܗ ΋͠൑ผʹࣦഊͨ͠ͳΒ
    ੜ੒ͨ͠஍ܗ͸
    

    ຊ෺Β͍͠஍ܗͰ͋Δ ্ԼͷωοτϫʔΫͷXΛަޓʹमਖ਼͢Δ ্ͷωοτϫʔΫͷXΛमਖ਼͢Δࡍʹ͸ԼͷωοτϫʔΫ͸ݻఆ
    ԼͷωοτϫʔΫ͕
    ΑΓ൑ผͰ͖ͳ͍஍ܗ͕ੜ੒͞ΕΔΑ͏ʹमਖ਼Λߦ͏
48. 48.

    ຊ෺ͬΆ͍஍ܗ࡞Δ ஍ܗ͕ຊ෺͔൑ผ͢Δ ຊ෺ͷ஍ܗ ੜ੒ͨ͠஍ܗ ΋͠൑ผʹࣦഊͨ͠ͳΒ
    ੜ੒ͨ͠஍ܗ͸
    ຊ෺Β͍͠஍ܗͰ͋Δ ԼͷωοτϫʔΫͷXΛमਖ਼͢Δࡍʹ͸্ͷωοτϫʔΫ͸ݻఆ
    ຊ෺ͷ஍ܗͱ্ͷωοτϫʔΫ͕ੜ੒ͨ͠΋ͷΛ
    ΑΓਖ਼ࣝ͘͠ผͰ͖ΔΑ͏ʹमਖ਼Λߦ͏

    ఢରతੜ੒ωοτϫʔΫ Generative Adversarial Network
49. 49.

    ຊ෺ͬΆ͍஍ܗ࡞Δ ஍ܗ͕ຊ෺͔൑ผ͢Δ ຊ෺ͷ஍ܗ ΋͠൑ผʹࣦഊͨ͠ͳΒ
    ੜ੒ͨ͠஍ܗ͸
    ຊ෺Β͍͠஍ܗͰ͋Δ ৚݅෇͖ఢରతੜ੒ωοτϫʔΫ Conditional Generative Adversarial

    Network εέονੜ੒
50. 50.

    ৚݅෇͖ఢରతੜ੒ωοτϫʔΫΛ༻͍ͨ
    ຊ෺Β͍͠஍ܗͷੜ੒ Éric Guérin, Julie Digne, Éric Galin, Adrien Peytavie,

    Christian Wolf, Bedrich Benes, and Benoît Martinez. 2017. Interactive example-based terrain authoring with conditional generative adversarial networks. ACM Trans. Graph. 36, 6, Article 228 (November 2017), 13 pages. DOI: https://doi.org/10.1145/3130800.3130804 ෼Ͱඳ͔Εͨεέον͔Βੜ੒͞Εͨ஍ܗ
51. 51.

    ("/Λ࢖ͬͨ%00.ͷεςʔδͷੜ੒ (JBDPNFMMP
    &EPBSEP
    FU
    BM
    l%00.
    -FWFM
    (FOFSBUJPO
    6TJOH
    (FOFSBUJWF
    "EWFSTBSJBM
    /FUXPSLTz
    
    *&&&
    (BNFT
    &OUFSUBJONFOU
    .FEJB
    $POGFSFODF
    (&.
     
    

    ("/Ͱ
    ࣮ࡍͷ%ήʔϜͷεςʔδΛ
    ຊ෺Β͘͠
    ੜ੒ͨ͠ࣄྫ ιʔείʔυ https://github.com/DanieleLoiacono/DoomGAN ࿦จ https://arxiv.org/abs/1804.09154
52. 52.

    %00. ᴈ໌ظͷ'14 ιʔείʔυ͕ެ։͞Ε͍ͯͯվ଄͠΍͍͢ҝ
    ౓ʑݚڀऀͷ͓΋ͪΌʹͳΔ https://github.com/id-Software/DOOM

53. 53.

    εςʔδΛ࡞Δ Ϛοϓ͕ຊ෺͔ࣝผ͢Δ ຊ෺ͷ
    %00.ͷεςʔδΛ
    ը૾ʹͨ͠΋ͷ ੜ੒ͨ͠εςʔδ ΋͠൑ผʹࣦഊͨ͠ͳΒ
    ຊ෺Β͍͠εςʔδͰ͋Δ ϝλσʔλ΍
    ෦԰਺౳ͷ
    

    ಛ௃ྔ
54. 54.

    ࿦จͷஶऀʹΑΔσϞಈը
    IUUQTZPVUVCF,';UK21 ΍ͬͨࣄ͕͋ΔਓͰͳ͍ͱΘ͔Γʹ͍͔͘΋͠Εͳ͍͚Ͳ
    ࣮ࡍʹ͋Γͦ͏ͳܗͷεςʔδ͕ग़དྷ্͕Δ

55. 55.

    $ZDMF("/ΛԠ༻ͨ͠υοτֆͷੜ੒ Chu Han, Qiang Wen, Shengfeng He, Qianshu Zhu, Yinjie

    Tan, Guoqiang Han, and Tien- Tsin Wong. 2018. Deep unsupervised pixelization. In SIGGRAPH Asia 2018 Technical Papers (SIGGRAPH Asia '18). ACM, New York, NY, USA, Article 243, 11 pages. DOI: https://doi.org/10.1145/3272127.3275082 http://www.shengfenghe.com/deep- unsupervised-pixelization.html ࣸਅ΍ΠϥετΛ
    ಥͬࠐΜͩΒ
    υοτֆʹͳͬͯग़ͯ͘Δ ͨͩॖখ͢ΔͷͰ͸ͳ͘
    ಛ௃Λ͖ͬ͘Γ࢒ͯ͠
    ϑϥοτͳ৭࢖͍ʹͳΔ ͜Ε͸
    ਓ͕ඳ͍ͨ৔߹
56. 56.

    A B B' ࣅ͍ͯΔ΄Ͳྑ͍ ը૾܈"ͱը૾܈#͕
    ରͰରԠ͍ͯ͠Δ৔߹
    "Λೖྗͱͯ͠ಘΒΕͨੜ੒෺ͱ#ͷҧ͍Λ
    ଛࣦؔ਺ʹֶͯ͠श͢Ε͹ྑ͍ "ͷΑ͏ͳը૾Λ#ͷΑ͏ͳը૾ʹม׵͍ͨ͠

57. 57.

    $ZDMF
    ("/ "Λ#ʹม׵ ຊ෺ͷ#ͱࠨͷੜ੒෺Λࣝผ #Λ"ʹม׵ ຊ෺ͷ"ͱӈͷੜ੒෺Λࣝผ A B ຊ෺ͷ#ͱੜ੒෺Λ
    ࣝผͰ͖ͳ͍ఔྑ͍ ຊ෺ͷ"ͱੜ੒෺Λ
    

    ࣝผͰ͖ͳ͍ఔྑ͍ #ˠ"ˠ#Ͱ
    ݩͱಉ͡ʹͳΔఔྑ͍ "ˠ#ˠ"Ͱ
    ݩͱಉ͡ʹͳΔఔྑ͍ Jun-Yan Zhu*, Taesung Park*, Phillip Isola, and Alexei A. Efros. "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks", in IEEE International Conference on Computer Vision (ICCV), 2017. (* indicates equal contributions)
58. 58.

    $ZDMF
    ("/ "Λ#ʹม׵ ຊ෺ͷ#ͱࠨͷੜ੒෺Λࣝผ #Λ"ʹม׵ ຊ෺ͷ"ͱӈͷੜ੒෺Λࣝผ A B #ˠ"ˠ#Ͱ
    ݩͱಉ͡ʹͳΔఔྑ͍ ຊ෺ͷ#ͱੜ੒෺Λ
    

    ࣝผͰ͖ͳ͍ఔྑ͍ ຊ෺ͷ"ͱੜ੒෺Λ
    ࣝผͰ͖ͳ͍ఔྑ͍ "ˠ#ˠ"Ͱ
    ݩͱಉ͡ʹͳΔఔྑ͍ ຊ෺ͷ"ͱຊ෺ͷ#͸ରͰରԠ͍ͯ͠Δඞཁ͸ͳ͍
    "ͷྫͱ#ͷྫ͕୔ࢁ͋Ε͹૒ํ޲ͷม׵͕ಘΒΕΔ
59. 59.

    υοτֆͷੜ੒ ֦େ ຊ෺ͷ"ͱӈͷੜ੒෺Λࣝผ A B ֦େˠॖখˠλονͰ
    ݩͱಉ͡ʹͳΔఔྑ͍ ॖখˠλονˠ֦େͰ
    ݩͱಉ͡ʹͳΔఔྑ͍ ࣸਅ΍Πϥετ

    υοτֆ ݩͷ৭ͱڥքΛ
    อ͍ͯͯΔఔྑ͍ ॖখ υοτֆͬΆ͍
    λονʹ͢Δ ຊ෺ͷυοτֆͱੜ੒෺Λࣝผ͢Δ ຊ෺ͱੜ੒෺Λ
    ࣝผͰ͖ͳ͍ఔྑ͍ ਅٯͷૢ࡞ʹ
    ͳ͍ͬͯΔఔྑ͍ http://www.shengfenghe.com/pixelization.html ͜͜ʹੜ੒ྫ͕୔ࢁ͋Δ
60. 60.

    υοτֆͷੜ੒ http://www.shengfenghe.com/deep-unsupervised-pixelization.html

61. 61.

    ("/Λ୔ࢁ૊Έ߹Θͤͨ
    ౎ࢢͷσΟςΟʔϧͷੜ੒ Tom Kelly, Paul Guerrero, Anthony Steed, Peter Wonka,

    and Niloy J. Mitra. 2018. FrankenGAN: guided detail synthesis for building mass models using style-synchonized GANs. In SIGGRAPH Asia 2018 Technical Papers (SIGGRAPH Asia '18). ACM, New York, NY, USA, Article 216, 14 pages. DOI: https://doi.org/10.1145/3272127.3275065 ೖྗ ग़ྗ http://geometry.cs.ucl.ac.uk/projects/2018/frankengan/
62. 62.

    ೉ॴ ૭౳ͷ഑ஔͷੜ੒ɺৄࡉͳܗঢ়ͷੜ੒ɺ৭ͷੜ੒
    ੜ੒͠ͳ͚Ε͹ͳΒͳ͍΋ͷ͕ଟ͍ શͯͷݐ෺͕ಉ͡ݟͨ໨ʹͳ͍ͬͯͨΒෆࣗવ
    ੜ੒݁Ռʹଟ༷ੑΛ࣋ͨͤΔඞཁ͕͋Δ

63. 63.

    ৚݅ cGAN ཚ਺ ৚݅ʹରԠ͢Δग़ྗ ֶशΛߦ͏ͨΊʹ͸
    ৚݅ͱਖ਼͍͠ग़ྗͷηοτ͕
    େྔʹඞཁ ৚݅෇͖ఢରతੜ੒ωοτϫʔΫͷ໰୊఺

64. 64.

    GAN ཚ਺ ৚݅ແ͠ఢରతੜ੒ωοτϫʔΫͷ໰୊఺ ग़ྗΛগ͠ม͍͑ͨͱࢥͬͯ΋
    ೖྗͷཚ਺ͷͲ͕͜มΘΕ͹ཉ͍͠ग़ྗʹͳΔͷ͔
    શ͘෼͔Βͳ͍

65. 65.

    ΦʔτΤϯίʔμ A A' ೖྗͱग़ྗ͕
    ࣅ͍ͯΔఔྑ͍ ͜ͷ෦෼͕ࡉ͍ͷͰ׬શͳ৘ใΛग़ྗʹ఻͑Δࣄ͸ग़དྷͳ͍ ݶΒΕͨ৘ใ͔ΒݩͷσʔλΛ෮ݩ͢ΔͨΊʹ
    ॏཁͳಛ௃͸ͲΕ͔Λֶश͢Δ͜ͱʹͳΔ

66. 66.

    ΦʔτΤϯίʔμ A A' ॏཁͳ ಛ௃ Τϯίʔμ σίʔμ

67. 67.

    ཚ਺͔Β"Λੜ੒ A' A ຊ෺ͱੜ੒෺Λ
    ࣝผͰ͖ͳ͍ఔྑ͍ Z ຊ෺ͷ"ͱੜ੒͞Εͨ"Λࣝผ ී௨ͷ("/

68. 68.

    ཚ਺͔Β"Λੜ੒ A' A ຊ෺ͱੜ੒෺Λ
    ࣝผͰ͖ͳ͍ఔྑ͍ Z ຊ෺ͷ"ͱੜ੒͞Εͨ"Λࣝผ *OGP("/ C ఢରతੜ੒ωοτϫʔΫ

    C' ࣅ͍ͯΔఔྑ͍ $͕"ͷॏཁͳಛ௃ʹؔ܎͍ͯ͠ΔͳΒ
    $ ; ˠ"ˠ$Ͱݩͷ$ʹ໭ΕΔഺ ΦʔτΤϯίʔμ $͕"ͷಛ௃Λίϯτϩʔϧ͢Δ
    ύϥϝʔλʹͳΔ "͔Β$Λ෮ݩ X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. Infogan: interpretable representation learning by information maximizing generative adversarial nets. In NIPS, 2016.
69. 69.

    ཚ਺Λແࢹͯ͠
    
    
    
    Λੜ੒ ຊ෺ͱੜ੒෺Λ
    ࣝผͰ͖ͳ͍ఔྑ͍ Z
    
    
    
    ΛࣝผͰ͖ͳ͍ Ϟʔυ่յ ࣝผث͕
    ݟ෼͚ΒΕͳ͍෺ͳΒ
    ͳΜͰ΋ྑ͍ ೖྗͷཚ਺;Λແࢹͯ͠
    

    ৗʹಉ͡Α͏ͳσʔλΛੜ੒͢Δ
    ωοτϫʔΫ͕ग़དྷ্͕Δࣄ͕͋Δ Mode Collapse
70. 70.

    ;͔Β"Λੜ੒ A' A ຊ෺ͱੜ੒෺Λ
    ࣝผͰ͖ͳ͍ఔྑ͍ ຊ෺ͷ"ͱੜ੒͞Εͨ"Λࣝผ 7"&("/ ఢରతੜ੒ωοτϫʔΫ ΦʔτΤϯίʔμ ࣅ͍ͯΔఔྑ͍

    ੜ੒ث͕;Λ࢖͍ͬͯΔͳΒ
    "ˠ;ˠ"Ͱݩͷ"ʹ໭ΕΔഺ Ϟʔυ่յΛى͜͢ͱ
    ΦʔτΤϯίʔμଆͷଛࣦ͕૿͑Δ "ͷॏཁͳಛ௃ΛؚΉ;Λ࡞Δ Rosca, Mihaela & Lakshminarayanan, Balaji & Warde-Farley, David & Mohamed, Shakir. (2017). Variational Approaches for Auto- Encoding Generative Adversarial Networks. Z
71. 71.

    Z A' A ຊ෺ͱੜ੒෺Λ
    ࣝผͰ͖ͳ͍ఔྑ͍ #JDZDMF("/ C' C Jun-Yan Zhu,

    Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang: “Toward Multimodal Image-to-Image Translation”, 2017; arXiv:1711.11586 ࣅ͍ͯΔఔྑ͍ ࣅ͍ͯΔఔྑ͍ D("/Ͱ৚݅Λࢦఆ
    *OGP("/͕
    ੜ੒෺ͷόϦΤʔγϣϯΛ࡞Γ
    7"&("/͕Ϟʔυ่յΛ๷͙
72. 72.

    A ֶश ৚݅ͱͦΕΛຬͨ͢"ͷϖΞΛ࢖ͬͯ
    Τϯίʔμͱੜ੒ثΛ࡞Δ C B ੜ੒ A "ͷॏཁͳಛ௃ͱ৚݅ͱଟ༷ੑͷૉ$͔Β
    "ͷಛ௃Λ࣋ͪ৚݅Λຬͨ͢ੜ੒෺Λ୔ࢁ࡞Δ

    #JDZDMF("/
73. 73.

    ্͔Βݟͨ
    ܗঢ় େࡶ೺ͳ ݐ෺ͷܗ ࣮ࡍͷ นͷը૾ ࣮ࡍͷ ԰ࠜͷը૾ ૭౳ͷ഑ஔͷநग़ Ԏಥ౳ͷ഑ஔͷநग़

    ԣ͔Β
    ݟͨܗঢ় ԰ࠜͷԜತ ԰ࠜͷ໛༷ ૭ͷ഑ஔ นͷ໛༷ นͷԜತ ߴղ૾౓ͳ
    ԰ࠜͷ໛༷ ߴղ૾౓ͳ
    ૭ͷ໛༷ ૭࿮ͷܗঢ় ߴղ૾౓ͳ
    นͷ໛༷ ԰ࠜͷԜತ นͷԜತ ৄࡉͳ ݐ෺ͷ ܗͱ໛༷ FrankenGAN
74. 74.

    χϡʔϥϧωοτϫʔΫ͕
    ༏Εͨࣗಈੜ੒Λߦ͏ͨΊʹ͸
    ଛࣦؔ਺ͷઃܭ͕伴ʹͳΔ ͦΕҎ֎͍͡ΕΔͱ͜Ζ͕ແ͍ͷͰ
    ग़͖ͯͯ΄͍͠෺Λ͍͔ʹͯ͠ଛࣦؔ਺Ͱදݱ͢Δ͔ʹͳΔ
    ͱ΋ݴ͑Δ

75. 75.

    ڧԽֶश Reinforcement Learning ୡ੒͍ͨ͠໨ඪʹۙͮ͘ํ๏Λֶश͢Δ ঢ়گΛೖྗͱͯ࣍͠ͷߦಈΛग़ྗ͢Δ
    ͨͩͦ͠ͷߦಈ͕ਖ਼͔͔ͬͨ͠͸͙͢ʹ֬ఆ͢Δͱ͸ݶΒͳ͍ a = f(s) ࠓͷঢ়ଶ

    ࣍ʹ
    औΔ΂͖ߦಈ
76. 76.

    ใु Reward Ձ஋
     ӈ୺ͷϚεʹͨͲΓண͘ͱՁ஋ͷใु͕ಘΒΕΔͱ͢Δ

77. 77.

    2ֶश Q Learning ͋ΔϚε͔ΒྡͷϚεʹҠಈͨ͠Β
    Ձ஋ͷ͓ๅ͕ஔ͍ͯ͋Δ࣌
    ͦͷҠಈʹ͸ͷՁ஋͕͋Δ Ձ஋
    

78. 78.

    2ֶश Q Learning Ձ஋ͷߦಈ͕Ͱ͖Δঢ়ଶʹ
    ભҠͰ͖Δߦಈʹ͸
    ΑΓগ͠গͳ͍Ձ஋͕͋Δͱߟ͑Δ Ձ஋
     Ձ஋
    

79. 79.

    2ֶश Q Learning ͍Ζ͍Ζͳঢ়ଶ͔ΒߦಈͷՁ஋ΛٻΊ͍ͯ͘ Ձ஋
     Ձ஋
     ͜ͷૢ࡞Λे෼ͳճ਺܁Γฦ͢ͱ
    ֤ঢ়ଶͰߦ͑Δ֤ߦಈͷՁ஋͕໌Β͔ʹͳΔ

80. 80.

    2ֶश Q Learning  ͋Δঢ়ଶ͔Β͋ΔߦಈΛߦͳͬͨ৔߹ͷՁ஋ͷද     

       ঢ়ଶ ߦಈ Ձ஋                   ʜ ঢ়ଶ΍ߦಈͷ਺͕ଟ͍໰୊Ͱ͸
    ͜ͷද͸େ͖͘ͳΓ͗ͯ͢هԱͰ͖ͳ͍
81. 81.

    /FVSBM
    'JUUFE
    2
    *UFSBUJPO ͦͷߦಈʹ͸
    ͜Ε͚ͩͷ
    Ձ஋͕͋Δ શͯͷߦಈͷՁ஋ΛهԱ͓ͯ͘͠୅ΘΓʹ
    ͋Δঢ়ଶ͔Β֤ߦಈΛߦͳͬͨ৔߹ͷՁ஋Λਪఆ͢Δ
    χϡʔϥϧωοτϫʔΫͷֶशΛߦ͏ ࠓ͜͏͍͏ঢ়ଶͰ
    ͜͏͍͏ߦಈΛ
    ͨ͠৔߹

    Riedmiller, Martin. Neural fitted Q iteration–first experiences with a data efficient neural reinforcement learning method. In: European Conference on Machine Learning. Springer, Berlin, Heidelberg, 2005. p. 317-328.
82. 82.

    ਪఆ
    ࣮ࡍ͸ ਪఆ
    ࣮ࡍ͸ ਪఆ
    ࣮ࡍ͸ ਪఆ
    ࣮ࡍ͸ Ձ஋ ʹḷΓண͚ͨ

    Ͱ΋ߦಈͷՁ஋͸ ͷ༧૝ͱҧͬͨ ࠓ"ͷঢ়ଶ ͜͏͍͏ଛࣦؔ਺ͰֶशΛߦ͏ ʹߦ͘ͱ͘Β͍ͷྑ͞ ͷྑ͞͸Ͳ͜ΖͰ͸ͳ͔ͬͨ ࣮ࡍͱͷ
    ޡࠩ "
83. 83.

    ਪఆ
    ࣮ࡍ͸ ਪఆ
    ࣮ࡍ͸ ਪఆ
    ࣮ࡍ͸ Ձ஋ ʹḷΓண͚ͳ͍৔߹΋ಉ༷ Ձ஋ ʹߦ͘ͷ͕ߴՁ஋

    
    
    
    ʹߦ͘ͷ͸ྑ͘ͳ͍ ࣮ࡍͱͷ
    ޡࠩ
84. 84.

    Q(st , at ) ← Q(st , at ) +

    α (R(st , at ) + γmax (Q(st+1 , at+1 )) − Q(st , at )) XΛগ͠मਖ਼͢Δࡍͷগ͠۩߹ ࠓͷߦಈͰಘΒΕΔใु ଍ݩʹ
    
    
    
    
    
    
    
    ͕͋Ε͹ͦͷՁ஋ मਖ਼લͷχϡʔϥϧωοτϫʔΫ͕ਪఆͨ͠
    ࣍ͷߦಈΛߦͳͬͯભҠͨ͠ઌͷߦಈͷՁ஋ मਖ਼લͷχϡʔϥϧωοτϫʔΫ͕ਪఆͨ͠
    ࠓͷߦಈͷՁ஋ ͜Εͱ͜ΕͷࠩΛখ͘͢͞ΔΑ͏ʹXΛमਖ਼͢Δ
85. 85.

    Q(st , at ) ← Q(st , at ) +

    α (R(st , at ) + γmax (Q(st+1 , at+1 )) − Q(st , at )) ޡࠩΛܭࢉ͢ΔաఔʹχϡʔϥϧωοτϫʔΫͷग़ྗ͕ඞཁ
    ֶशத͸XΛαϯϓϧຖʹมߋ͍ͯ͠Δҝ͜ͷ஋͕҆ఆ͠ͳ͍ ਪఆͨ͠Ձ஋͕
    ٻΊͨՁ஋ʹ͍ۙ΄Ͳྑ͍ ࣌ʑίϐʔ Xͷमਖ਼͕Ձ஋ͷਪఆʹ
    ௚͙ʹ൓ө͞Εͳ͍Α͏ʹͯ͠
    ଛࣦؔ਺Λ҆ఆͤ͞Δ ਂ૚2ωοτϫʔΫ Deep Q Network Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.
86. 86.

    Vlad Firoiu, William F. Whitney, Joshua B. Tenenbaum Beating the

    World's Best at Super Smash Bros. with Deep Reinforcement Learning. arXiv preprint arXiv:1702.06230, 2017. ਂ૚QωοτϫʔΫΛ༻͍ͨ େཚಆεϚογϡϒϥβʔζDXͷAIͷ࡞੒ https://youtu.be/dXJUlqBsZtE ڧԽֶश
    ϑΝϧίϯ ϓϩήʔϚʔ
    ϑΝϧίϯ "*͕ϓϩͱઓͬͯউͬͨࣄΛใ͡Δهࣄ https://www.businessinsider.com/super-smash-bros- pro-slox-mafia-artificial-intelligence-2017-2
87. 87.

    ਓؒ͸ը໘ΛݟͯߦಈΛܾఆ͢Δ "*͸ϝϞϦΛݟͯߦಈΛܾఆ͢Δ ͜ͷҧ͍͕͠͹͠͹
    ΤϑΣΫτ͕දࣔ͞ΕΔલ͔Βߦಈͩ͢͠ͱ͍ͬͨ
    ཧෆਚʹڧ͍"*ΛੜΈग़͢

88. 88.

    χϡʔϥϧωοτϫʔΫʹ
    ը໘͔Βঢ়ଶΛ൑அ͢Δͱ͜Ζ͔Β
    ΍ΒͤΔ͜ͱ͸ग़དྷͳ͍͔

89. 89.

    Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou,

    Daan Wierstra, Martin Riedmiller Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013. Atari 2600ͷը໘Λೖྗͱͯ͠ ద੾ͳߦಈΛબ୒͢ΔڧԽֶश
90. 90.

    Guillaume Lample, Devendra Singh Chaplot Playing FPS Games with Deep

    Reinforcement Learning. In: AAAI. 2017. p. 2140-2146. ը໘Λೖྗͱͯ͠ DOOMΛ߈ུ͢ΔڧԽֶश https://github.com/fhennecker/deepdoom https://youtu.be/L_9F9bO3-mI ࿦จͷஶऀʹΑΔσϞ ஶऀͱ͸ผͷਓʹΑΔ࣮૷
91. 91.

    Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou,

    Daan Wierstra, Martin Riedmiller Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013. ը໘͔Β͙͢ʹ࣍͢΂͖ࣄ͕Θ͔ΔήʔϜͰ͸
    ڧԽֶशͷείΞ͸ਓؒͷείΞΛ௒͑Δ ௕ظతͳઓུΛཁ͢ΔήʔϜͰ͸ਓ͕ؒѹউ͢Δ
92. 92.

    ಛผͳ஍ܗʹԊͬͨಈ͖͸
    ͦΕʹ߹ΘͤͯΩϟϓνϟΛߦͳ͓ͬͯ͘ඞཁ͕͋Δ ݹయతͳϞʔγϣϯΩϟϓνϟʹΑΔ
    Ξχϝʔγϣϯͷݶք

93. 93.

    Daniel Holden, Taku Komura, and Jun Saito. 2017. Phase-functioned neural

    networks for character control. ACM Trans. Graph. 36, 4, Article 42 (July 2017), 13 pages. DOI: https:// doi.org/10.1145/3072959.3073663 http://theorangeduck.com/page/phase-functioned-neural-networks-character-control ϞʔγϣϯΩϟϓνϟͱ஍ܗͷϖΞͰ
    χϡʔϥϧωοτϫʔΫͷֶशΛߦ͍
    ༩͑ΒΕͨ஍ܗΛࣗવʹา͘ಈ͖Λ
    ੜ੒͢Δ ஍ܗ ஍ܗʹԊͬͨ า͖ํ
94. 94.

    ࣍ʹલʹग़͢΂͖଍͕पظతʹʹมԽ͍ͯ͠Δ + + = = ಉ͡஍ܗͰ΋ λΠϛϯάʹΑͬͯ
    ద੾ͳಈ͖ํ͸มΘΔ w(0) w(1)

    w(2) w(3) पظతʹ੾ΓସΘΔͭͷXΛ࣋ͭωοτϫʔΫͰਪఆ 1IBTF'VODUJPOFE
    /FVSBM
    /FUXPSL
95. 95.

    = = λΠϛϯάʹΑͬͯ
    ద੾ͳಈ͖ํ͸มΘΔ w(0) w(1) w(2) w(3) पظతʹ੾ΓସΘΔͭͷXΛ࣋ͭωοτϫʔΫͰਪఆ े෼ͳྔͷֶशσʔλΛूΊΔͨΊʹ
    

    ࣮ࡍʹ༷ʑͳ஍ܗΛ࣌ؒʹΘͨͬͯา͖ճͬͨ৔߹ͷಈ͖Λ
    ϞʔγϣϯΩϟϓνϟͰूΊΔ ΋͏ͪΐͬͱָͰ͖ͳ͍͔
96. 96.

    ڧԽֶशʹΑΔาߦϞʔγϣϯͷੜ੒ Xue Bin Peng, Glen Berseth, Kangkang Yin, and Michiel

    Van De Panne. 2017. DeepLoco: dynamic locomotion skills using hierarchical deep reinforcement learning. ACM Trans. Graph. 36, 4, Article 41 (July 2017), 13 pages. DOI: https://doi.org/ 10.1145/3072959.3073602 https://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/
97. 97.

    ʮา͘ҝͷ଍ͷಈ͔͠ํ͕ະ஌ͷঢ়ଶ͔Β໨ඪ஍఺·Ͱา͚ͨΒใुʯ ͜Ε͸ใु͕ԕ্͗ͯ͢ख͍͔͘ͳ͍ ? ? ? 2ֶश͸ใुΛۮવൃݟ͢Δ·Ͱ͸
    ϥϯμϜͳࢼߦ͔͠ग़དྷͳ͍ ਪఆ ਪఆ ใु͸ΰʔϧ͚ͩͰͳ͘
    

    ͦ͜·ͰͷಓఔΛ༠ಋ͢ΔΑ͏ʹ
    ഑ஔ͞Ε͍ͯΔඞཁ͕͋Δ ?
98. 98.

    ͜Ε͸ใु͕ԕ্͗ͯ͢ख͍͔͘ͳ͍ ·ͣԿॲΛ౿ΜͰ໨ඪʹͨͲΓண͔͚ͩ͘Λ൑அ͢Δ ݱࡏ஍
    ஍ܗ
    ໨ඪ
    ࢟੎ ԿॲΛ౿Ή͔ ʮา͘ҝͷ଍ͷಈ͔͠ํ͕ະ஌ͷঢ়ଶ͔Β໨ඪ஍఺·Ͱา͚ͨΒใुʯ

99. 99.

    ݱࡏ஍
    ஍ܗ
    ໨ඪ
    ࢟੎ ԿॲΛ౿Ή͔ ࣍ͷҰาͷҝʹͲ͏ಈ͔͘ ࣍ͷҰาΛ
    ࢦఆ͞ΕͨҐஔͰ౿ΊͨΒ
    ใु ໨ඪҐஔ·Ͱ
    

    า͘͜ͱ͕Ͱ͖ͨΒ
    ใु ҰาΛਖ਼͘͠า͘͜ͱΛֶश͢ΔڧԽֶशͱ
    ໨ඪ஍఺·ͰͲͷΑ͏ͳܦ࿏Ͱา͔͘Λֶश͢ΔڧԽֶशΛ
    ૊Έ߹ΘͤΔ͜ͱͰ
    ໨ඪ஍఺·Ͱา͘ҝͷಈ͖ํΛशಘͤ͞Δ ඵʹճ ඵʹճ
100. 100.

     ڧԽֶशʹΑΔө૾͔Βͷಈ͖ͷशಘ Xue Bin Peng, Angjoo Kanazawa, Jitendra Malik, Pieter Abbeel,

     and Sergey Levine. 2018. SFV: reinforcement learning of physical skills from videos. In SIGGRAPH Asia 2018 Technical Papers (SIGGRAPH Asia '18). ACM, New York, NY, USA, Article 178, 14 pages. DOI: https://doi.org/10.1145/3272127.3275014
101. 101.

     Xue Bin Peng, Angjoo Kanazawa, Jitendra Malik, Pieter Abbeel, and

     Sergey Levine. 2018. SFV: reinforcement learning of physical skills from videos. In SIGGRAPH Asia 2018 Technical Papers (SIGGRAPH Asia '18). ACM, New York, NY, USA, Article 178, 14 pages. DOI: https://doi.org/10.1145/3272127.3275014 ө૾͔Β࢟੎Λਪఆ͠
     ͓खຊͱͳΔಈ͖Λ࡞Δ ΩϟϥΫλʔ͕෺ཧతʹՄೳͳൣғͰ͓खຊʹ͍ۙಈ͖Λֶश͢Δ ෺ཧγϛϡϨʔγϣϯԼͰ
     ಈ͘ΩϟϥΫλʔ͕
     ֤ϑϨʔϜʹ͓͍ͯ
     ͓खຊʹ͍ۙϙʔζΛ͍ͯ͠Δఔ
     ߴ͍ใु
102. 102.

     ڧԽֶशʹΑΔ෰ΛணΔಈ͖ͷशಘ Alexander Clegg, Wenhao Yu, Jie Tan, C. Karen Liu,

     and Greg Turk. 2018. Learning to dress: synthesizing human dressing motion via deep reinforcement learning. In SIGGRAPH Asia 2018 Technical Papers (SIGGRAPH Asia '18). ACM, New York, NY, USA, Article 179, 10 pages. DOI: https://doi.org/10.1145/3272127.3275048 ෰ʹ͸෍γϛϡϨʔγϣϯΛߦ͍͍ͨ ࣄલʹΩϟϓνϟͨ͠ಈ͖Ͱ͸
     ର৅ͷΩϟϥΫλʔ͸
     ਖ਼͘͠෰ΛணΕͳ͍͔΋͠Εͳ͍ https://www.cc.gatech.edu/~aclegg3/projects/LearningToDress.html ڧԽֶशͰΩϟϥΫλʔʹ
     ࣗྗͰ෰ΛணΔํ๏Λֶशͯ͠΋Β͏
103. 103.

     r(s) = w1 rp (s) + w2 rd (s) +

     w3 rg (s) + w4 rt (s) + w5 rr (s) ෰ΛணΔڧԽֶशͷใु rp (s) ਐḿใु
     କΛ௨աͨ͠࿹΍಄ͷ௕͞ʹൺྫ͢Δใु rd (s) rg (s) rr (s) ͱΓ͋͑ͣକʹ࿹Λ௨ͦ͏ͱ͢ΔΑ͏ʹ༠ಋ มܗใु
     ෰͕ҾͬுΒΕͯ৳ͼ͍ͯΔ΄Ͳݮগ͢Δใु ෰Λഁ͘Α͏ͳಈ͖Λආ͚ΔΑ͏ʹ༠ಋ
104. 104.

     r(s) = w1 rp (s) + w2 rd (s) +

     w3 rg (s) + w4 rt (s) + w5 rr (s) ෰ΛணΔڧԽֶशͷใु rp (s) ਐḿใु
     କΛ௨աͨ͠࿹΍಄ͷ௕͞ʹൺྫ͢Δใु rd (s) มܗใु
     ෰͕ҾͬுΒΕͯ৳ͼ͍ͯΔ΄Ͳݮগ͢Δใु rg (s) ଌ஍ใु
     ෰͕ഽʹ઀৮͍ͯ͠Δׂ߹ʹൺྫ͢Δใु rr (s) ͱΓ͋͑ͣକʹ࿹Λ௨ͦ͏ͱ͢ΔΑ͏ʹ༠ಋ ෰Λഁ͘Α͏ͳಈ͖Λආ͚ΔΑ͏ʹ༠ಋ ෰Λ਎ʹ͚͍ͭͯΔঢ়ଶΛ໨ࢦ͢Α͏ʹ༠ಋ ΰʔϧใु
     ෰Λணͨঢ়ଶʹࣅ͍ͯΔ΄Ͳ૿Ճ͢Δใु କ͔Β಄Λग़ͦ͏ͱͨ͠Γ͠ͳ͍Α͏ʹ༠ಋ ෰ͷछྨʹΑͬͯ
     
     
     
     ͷׂ߹Λௐ੔ w
105. 105.

     ڧԽֶश͕༏ΕͨߦಈΛੜ੒͢ΔͨΊ͸
     ใुͷઃܭ͕伴ʹͳΔ ͦΕҎ֎͍͡ΕΔͱ͜Ζ͕ແ͍ͷͰ
     ใुΛͲͷΑ͏ʹઃఆͯ͠ձಘͯ͠ཉ͍͠ߦಈʹ༠ಋ͢Δ͔ʹͳΔ
     ͱ΋ݴ͑Δ

106. 106.

     ϦΞϧλΠϜϨΠτϨʔγϯά ޫͷ෺ཧతͳৼΔ෣͍ΛγϛϡϨʔτ͢ΔࣄͰ
     ֤ϐΫηϧͷ৭Λܾఆ͢Δ ͋Δ఺͕์ࣹ͢ΔޫΤωϧΪʔ͸
     ͦͷ఺͕ࣗΒൃͨ͠ޫΤωϧΪʔͱ
     ͋ΒΏΔํ޲͔Βͦͷ఺ʹ౸ୡͨ͠ޫΤωϧΪʔͷ૯࿨͔Β
     ͦͷ఺͕ٵऩͨ͠ޫΤωϧΪʔΛҾ͍ͨ΋ͷʹͳΔ ͜ͷܭࢉʹ͸͋ΒΏΔํ޲ʹ͍ͭͯͷੵ෼͕܁Γฦ͠ݱΕΔ ϞϯςΧϧϩੵ෼Ͱղ͘ͷ͕Ұൠత͕ͩ
     ϦΞϧλΠϜͱͳΔͱे෼ͳαϯϓϧ਺Λ֬อͰ͖ͳ͍ͨΊ
     

     ϊΠζͩΒ͚ͷϨϯμϦϯά݁Ռ͕ग़དྷ্͕Δ
107. 107.

     ϊΠζͩΒ͚ͷϨϯμϦϯά݁ՌΛΦʔτΤϯίʔμʹ௨ͯ͠
     ϊΠζ͕ແ͔ͬͨ৔߹ͷը૾Λਪఆͤ͞Δ Chakravarty R. Alla Chaitanya, Anton S. Kaplanyan, Christoph

     Schied, Marco Salvi, Aaron Lefohn, Derek Nowrouzezahrai, and Timo Aila. 2017. Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder. ACM Trans. Graph. 36, 4, Article 98 (July 2017), 12 pages. DOI: https://doi.org/10.1145/3072959.3073601 https://research.nvidia.com/publication/interactive-reconstruction-monte-carlo-image-sequences-using-recurrent-denoising
108. 108.

     ࣗಈੜ੒͸༩͑ΒΕͨ৚݅ʹͰ͖Δ͚͍ͩۙ෼෍Λอͱ͏ͱ͢Δ ਓ͕࡞඼Λ໘ന͍ͱײ͡Δͷ͸༧૝Λཪ੾ΒΕͨ࣌Ͱ͋Δ ݱࡏͷػցֶश͸ήʔϜ։ൃΛܶతʹָʹ͢Δ͕
     ήʔϜͷ໘നͦ͞ͷ΋ͷΛੜΈग़͢ʹ͸ࢸ͍ͬͯͳ͍

109. 109.

     ήʔϜΛߏ੒͢Δཁૉ͕
     ήʔϜʹݱ࣮ຯΛ༩͑ΔͨΊʹ࡞Δ෺ͳͷ͔
     ήʔϜΛ໘ന͘͢ΔͨΊʹ࡞Δ΋ͷͳͷ͔
     Λߟ͑Α͏ લऀ͸ػցֶशͰੜ੒ͨ͠ํ͕ྑ͍͔΋͠Εͳ͍ ޙऀ͸ਓ͕ؒ࡞ͬͨํ͕ྑ͍͔΋͠Εͳ͍