Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Reinforcement Learning from classic to DQN

Wonseok Jung
September 13, 2018
Research
0
72

Reinforcement Learning from classic to DQN

고전강화학습부터 DQN 까지 설명 자료 입니다.

Wonseok Jung

September 13, 2018
Tweet

More Decks by Wonseok Jung

See All by Wonseok Jung
Ai for business -self car driving
wonseokjung
0
170
reinforcement_learning_.pdf
wonseokjung
2
1.5k
원석이의 모두연에서 강화학습 보석되기
wonseokjung
0
390
NeuralIPS
wonseokjung
0
300
Introduction Deep Reinforcement Learning
wonseokjung
0
120
Deep reinforcemenet learning -2
wonseokjung
0
160
Deep Reinforcement Learning - Introduction
wonseokjung
1
600
How to become a datascientist ?
wonseokjung
2
2.3k
Review of Taylor series
wonseokjung
1
110

Other Decks in Research

See All in Research
言語間転移学習で大規模言語モデルを賢くする
ikuyamada
6
3.3k
オープンな日本語埋め込みモデルの選択肢 / Exploring Publicly Available Japanese Embedding Models
nttcom
14
5.5k
Introduction of NII S. Koyama's Lab (AY2024)
skoyamalab
0
110
サウナでのプロジェクションマッピングの可能性の検討 / EC71koizumi
yumulab
0
170
Sosiaalisen median katsaus 02/2024
hponka
0
2.6k
[ICLR'24] Towards Assessing and Benchmarking Risk-Return Tradeoff of OPE
harukakiyohara_
0
200
CVPR2023 EarthVision Workshopより衛星画像関連論文紹介 / Satellite Imaging Processing Papers in CVPR2023 EarthVision Workshop
nttcom
0
120
Generative Spoken Dialogue Language Modeling [対話論文読み会@電通大]
yuta0306
1
130
Azure Arc-enabled Serversを利用した ハイブリッド・マルチクラウド環境の管理 / Managing Hybrid Multi-cloud Environments with Azure Arc-enabled Servers
nttcom
0
210
Prompt Tuning から Fine Tuning への移行時期推定
icoxfog417
17
7k
継続的な研究費獲得のための考え方
moda0
0
150
AIを前提とした体験の実現に向けて/toward_ai_based_experiences
monochromegane
1
240

Featured

See All Featured
A Philosophy of Restraint
colly
197
16k
Helping Users Find Their Own Way: Creating Modern Search Experiences
danielanewman
20
1.9k
A Tale of Four Properties
chriscoyier
151
22k
A better future with KSS
kneath
231
16k
Building a Modern Day 
E-commerce SEO Strategy
aleyda
17
6.4k
Fontdeck: Realign not Redesign
paulrobertlloyd
76
4.9k
What’s in a name? Adding method to the madness
productmarketing
PRO
16
2.6k
From Idea to $5000 a Month in 5 Months
shpigford
377
45k
Art, The Web, and Tiny UX
lynnandtonic
289
19k
Rebuilding a faster, lazier Slack
samanthasiow
73
8.2k
Building a Scalable Design System with Sketch
lauravandoore
456
32k
Learning to Love Humans: Emotional Interface Design
aarron
267
39k

Transcript

  1. ъച೟ण੄੉ۿҗपઁ ੿䤯ࢳ 

  2. ъച೟ण੄੉ۿҗपणਸ߽೯ ੋҕ૑מगಌܻ݃য়ܳझझ۽ٜ݅ӝਤೠ Ѣ੄ݽٚѪ੉׸ѹ੓णפ׮

  3. ࣗѐ ੿ਗࢳোҳਗ City University of New York -Baruch College Data

    Science ੹ҕ ConnexionAIোҳਗ Freelancer Data Scientist ݽف੄োҳࣗъച೟णোҳਗ Github: https://github.com/wonseokjung Facebook: https://www.facebook.com/ws.jung.798 Blog: https://wonseokjung.github.io/

  4. 1. Dynamic Programming a. Policy iteration b. Value iteration 2.

    Monte Carlo method 3. Temporal-Difference Learning a. Sarsa b. Q-learning  ٩۞׬೐ۨ੐ਕ௼ாۄझࣗѐ߂गಌܻ݃য়ജ҃ҳ୷  %2/ਸ੉ਊೠੋҕ૑מगಌܻ݃য়ٜ݅ӝ ࣽࢲ

  5. 1. Dynamic Programming a. Policy iteration b. Value iteration 2.

    Monte Carlo method 3. Temporal-Difference Learning a. Sarsa b. Q-learning  गಌܻ݃য়ജ҃ҳ୷߂٩۞׬೐ۨ੐ਕ௼ாۄझࣗѐ  %2/ਸ੉ਊೠੋҕ૑מगಌܻ݃য়ٜ݅ӝ .PEFMGSFF .PEFMCBTFE %FFQMFBSOJOH
 
 3- पण

  6. 1. Dynamic Programming a. Policy iteration b. Value iteration 2.

    Monte Carlo method 3. Temporal-Difference Learning a. Sarsa b. Q-learning  गಌܻ݃য়ജ҃ҳ୷߂٩۞׬೐ۨ੐ਕ௼ாۄझࣗѐ  %2/ਸ੉ਊೠੋҕ૑מगಌܻ݃য়ٜ݅ӝ (SJEXPSME पण

  7. #FGPSF%FFQMFBSOJOH "GUFS%FFQMFBSOJOH 5BCVMBS *NBHF UFYU WPJDFj ജ҃ࢶఖ੄੉ਬ

  8. $MBTTJD3- %FFQ-FBSOJOH Ҋ੹ъച೟ण੉઺ਃೠ੉ਬ

  9. 2MFBSOJOH $//%2/ %2/

  10. п-FWFM੄4UBUFо׮ܰӝٸޙী(FOFSBMBHFOUܳ
 ٜ݅ӝоয۵׮ ಽܻ૑ঋ਷ޙઁٜ

  11. गಌܻ݃য়֤ޙ University of California, Berkeley ICML 2017 Curiosity-driven Exploration by

    Self-supervised Prediction

  12. IUUQTHJUIVCDPNXPOTFPLKVOH,*14@3FJOGPSDFNFOU पण੗ܐח $PEF +VQZUFS/PUFCPPL઱ࢳ ࢸ஖ߨݽفઁҕ ҾӘೠ੼੉੓ਵदݶѐੋ੸ਵ۽োۅ઱ࣁਃ

  13. Markov Decision Process

  14. 3FUVSOPG&QJTPEF &QJTPEFزউ3FUVSOػ3FXBSE੄೤ 5PUBM3FXBSE

  15. %JTDPVOUFE3FUVSO %JTDPVOUFEGBDUPSо੸ਊػ3FXBSE੄೤ 5PUBM3FXBSEXJUI%JTDPVOUFE

  16. .%1ীࢲ੄Y(SJEXPSME Grid World Environment

  17. .%1ীࢲ੄Y(SJEXPSME 4UBUFӒܻ٘੄ઝ಴ "DUJPO࢚ ೞ ઝ ਋ 3FXBSEೣ੿ ݾ಴
 5SBOTJUJPO1SPCBCJMJUZ %JTDPVOUGBDUPS

    3FXBSE  3FXBSE  4UBUF "DUJPO Grid World Environment

  18. 4UBUFWBMVFGVODUJPO 1PMJDZܳٮܲTUBUFWBMVFGVODUJPO 4UBUFWBMVF

  19. 4UBUFWBMVFGVODUJPO 1PMJDZܳٮܲTUBUFWBMVFGVODUJPO 4UBUFWBMVF

  20. "DUJPO7BMVFGVODUJPO 1PMJDZܳٮܲBDUJPOWBMVFGVODUJPO  4UBUFBDUJPOWBMVF

  21. #FMMNBOFRVBUJPO "'VOEBNFOUBMQSPQFSUZPGWBMVFGVODUJPO

  22. 0QUJNBM1PMJDZܳ଺੗TUBUF 7BMVFܳ୭؀۽ 0QUJNBMTUBUFWBMVFGVODUJPO

  23. 0QUJNBM1PMJDZܳ଺੗TUBUFBDUJPO 7BMVFܳ୭؀۽ 0QUJNBMTUBUFBDUJPOWBMVFGVODUJPO

  24. #FMMNBOFRVBUJPO 0QUJNBMJUZ #FMMNBOPQUJNBMJUZFRVBUJPOW

  25. #FMMNBOFRVBUJPO 0QUJNBMJUZ #FMMNBOPQUJNBMJUZFRVBUJPOR

  26. ൒ܴ .%1 3FUVSO&QJTPEF 3FUVSO&QTJTPEF EJTDPVOU  4UBUFWBMVFGVODUJPO "DUJPOWBMVFGVODUJPO 0QUJNBM1PMJDZ #FMMNBO&RVBUJPO

    #FMMNBOPQUJNBMFRVBUJPO #FMMNBO&RVBUJPO 0QUJNBM1PMJDZ

  27. Dynamic Programming

  28. ઑѤ State, Reward, Action

  29. ઑѤ Transition Probability ژೠ઱য઎׮

  30. Dynamic Programming
 7BMVFGVODUJPOਸࢎਊೞৈࠁ׮ա਷1PMJDZ ܳ଺ӝਤ೧ҳઑചदఃҊ੿ܻೡࣻ੓׮ Dynamic programming੄Key idea!

  31. Dynamic programming

  32. Y(SJEXPSMEীࢲ%ZOBNJD1SPHSBNNJOH Grid World Environment

  33. Y(SJEXPSME 4UBUFӒܻ٘੄ઝ಴ "DUJPO࢚ ೞ ઝ ਋ 3FXBSEೣ੿ ݾ಴
 5SBOTJUJPO1SPCBCJMJUZ %JTDPVOUGBDUPS

    3FXBSE  3FXBSE  അ੤TUBUF "DUJPO Grid World Environment ׮਺TUBUF ׮਺TUBUF

  34. 6QEBUF3VMF
 #FMMNBOFRVBUJPOਸࢎਊೞৈসؘ੉౟ೠ׮ 4UBUF

  35. فઙܨ੄0QUJNBM7BMVFGVODUJPOT 4UBUF7BMVF #FMMNBOPQUJNBMJUZFRVBUJPOT୽઒

  36. فઙܨ੄0QUJNBM7BMVFGVODUJPOT "DUJPO7BMVF #FMMNBOPQUJNBMJUZFRVBUJPOT୽઒

  37. Dynamic Programming
 فઙܨ੄୭੸7BMVFGVODUJPO State-action Value function

  38. 1PMJDZ*UFSBUJPO 7BMVF*UFSBUJPO Dynamic Programming

  39. 1PMJDZJUFSBUJPO 1.Policyܳٮۄ state-valueܳ҅࢑ೞ੗ Policy Evaluation ؊જ਷Policyܳ଺੗ Policy Improvement ୭੸੄1PMJDZܳ ଺ӝਤೠفо૑

    җ੿

  40. Policy iteration- Policy Evaluation 6QEBUF3VMFਸࢎਊೞৈ&WBMVBUJPOਸೠ׮ 7BMVFVQEBUF 1PMJDZ 5SBOTJUJPO 1SPCBCJMJUZ 3FXBSE

    /FYU4UBUF
 FTUJNBUFEWBMVF

  41.  ݽٚTUBUFܳ7 T ਵ۽ୡӝചदఅ׮ пTUBUFܳ6QEBUF3VMFਸࢎਊೞৈ7 T ܳসؘ੉౟ೠ׮ Policy iteration- Policy

    Evaluation সؘ੉౟ೞݴ7 T ੄߸ച۝੉ݒ਋੘ਸٸসؘ੉౟ܳݥ୸׮ Policyܳٮۄ state-valueܳ҅࢑ೞ੗

  42. Policy iteration- Improvement 1PMJDZܳٮۄ7BMVFGVODUJPOਸ҅࢑ೠ੉ਬח؊ա਷ 1PMJDZܳ଺ӝਤ೧ࢲ੉׮ (SFFEZ1PMJDZ

  43. Policy iteration- Improvement (SFFEZ1PMJDZ੸ਊ

  44. 1PMJDZJUFSBUJPO 1PMJDZJUFSBUJPO਷0QUJNBMQPMJDZܳ଺ਸٸө૑ 1PMJDZ&WBMVBUJPOҗ1PMJDZ*NQSPWFNFOUܳ߈ࠂೠ׮

  45. (SJE8PSME&OWJSPONFOU Y(SJEXPSME

  46. Y(SJEXPSME 4UBUFӒܻ٘੄ઝ಴ "DUJPO࢚ ೞ ઝ ਋ 3FXBSEೠ஢਑૒ੌٸ݃׮ 5SBOTJUJPO1SPCBCJMJUZ %JTDPVOUGBDUPS (SJE8PSME&OWJSPONFOU1PMJDZJUFSBUJPO

    3FXBSE  (PBM "DUJPO (PBM 3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE  3FXBSE 

  47. (SJE8PSME&OWJSPONFOU1PMJDZJUFSBUJPO Lੌٸ ୡӝച 7L (SFFE1PMJDZ     

                       

  48. 7L (SFFE1PMJDZ    L    

                      (SJE8PSME&OWJSPONFOU1PMJDZJUFSBUJPO

  49. 7L (SFFE1PMJDZ    L    

        (SJE8PSME&OWJSPONFOU1PMJDZJUFSBUJPO              

  50. 7L (SFFE1PMJDZ    LJOG    

        (SJE8PSME&OWJSPONFOU1PMJDZJUFSBUJPO              

  51. 1PMJDZJUFSBUJPO दো

  52. 1PMJDZ*UFSBUJPO 7BMVF*UFSBUJPO Dynamic Programming

  53. 7L (SFFE1PMJDZ    Lࣻ۴ೞݶ    

        (SJE8PSME&OWJSPONFOU7BMVFJUFSBUJPO              

  54. 7BMVF*UFSBUJPO ߈ࠂೞ૑ঋח׮ 4UBUF "DUJPO

  55. 7BMVFJUFSBUJPO दো

  56. .PEFM੉হ׮ݶ  पઁ۽҃೷ਸ೧ࠁݴജ҃җ࢚ഐ੘ਊਸ೧ঠೠ׮

  57. Monte Carlo method

  58. .POUF$BSMPNFUIPEח%ZOBNJDQSPHSBNJOH୊ۢ ݽٚ੿ࠁܳঌҊद੘ೞחѪ੉ইצ पઁ۽҃೷ਸೞݴജ҃җ࢚ഐ੘ਊਸೠ׮ .POUF$BSMP

  59. पઁ۽҃೷ਸೞݴߓ਋חߑߨ੉જ਷੼਷FOWJSPONFOU੄ ੿ࠁоহযبपઁ۽҃೷ਸೞݴPQUJNBMCFIBWJPSਸ੉ܖӝ ⮚ٸޙ .POUF$BSMP

  60. .POUF$BSMP .POUF$BSMPחFQJTPEFCZFQJTPEF۽সؘ੉౟ೠ׮ ীೖࣗ٘੄݃૑݄झప੉౟UFSNJOBMTUBUFө૑ оࢲসؘ੉౟ೠ׮ .POUF$BSMPח҃೷ਸೞݴSFUVSOػTBNQMFਸ੉ਊೞৈ TUBUFBDUJPOWBMVFܳಣӐೞৈসؘ੉౟ೠ׮

  61. (PBM .POUF$BSMP(SJE8PSME ՘ө૑оࠄٍ6QEBUF 4UBSU

  62. .POUF$BSMP दো

  63. Temporal-Difference Learning

  64. ݅ডъച೟णਸ؀಴ೡࣻ੓חই੉٣যо੓׮ݶӒ Ѫ਷5% UFNQPSBMEJGGFSFODF MFBSOJOHੌѪ੉׮ 4VUUPO 5FNQPSBM%JGGFSFODF-FBSOJOH

  65. 5FNQPSBM%JGGFSFODF-FBSOJOH .POUF$BSMP %ZOBNJDQSPHSBNNJOH .POUF$BSMP୊ۢݽ؛হ੉҃೷ਸాೞৈWBMVFܳஏ੿ೞݴ %1୊ۢ՘ө૑о૑ঋইب઺р઺рWBMVFܳFTUJNBUFೞחѪ੉оמ

  66. 5FNQPSBM%JGGFSFODF-FBSOJOH അ੤TUBUFীࢲBDUJPOਸࢶఖೞݴ߉ਸ3FXBSEҗ׮਺4UBUFীEJTDPVOUGBDUPSо੸ਊػ TUBUFWBMVFܳFTUJNBUFೞݴVQEBUFೠ׮ .POUF$BSPMPীࢲ੄(Uܳঌইঠসؘ੉౟оמ

  67. 5%੄੢੼  .POUF$BSMP੄ъ੼ੋജ҃੄ݽ؛ਸঌ૑ޅ೧بࢎਊоמ %ZOBNJDQSPHSBNNJOHীࢲ୊ۢ0OMJOF೟ण੉׮ ՘ө૑ӝ׮ܻ૑ঋইب ઺р઺рVQEBUFооמೞӝীFQJTPEFо ݆੉ӡѢա഑਷DPOUJOVFೠNPEFMীࢲࢎਊೞӝજ׮

  68. 5%੄ই੉٣য 5FNQPSBM%JGGSFOFDF-FBSOJOH 4BSTB 2MFBSOJOH 5FNQPSBM%JGGSFOFDF-FBSOJOH੉4BSTB৬2MFBSOJOH੄߄ఔই੉٣যоغ঻׮ 0OQPMJDZ 0GGQPMJDZ

  69. 4BSTB 2MFBSOJOH Temporal-Diffrenece Learning

  70. 4BSTB POQPMJDZߑߨਸࢎਊೞח4BSTB TUBUFWBMVFGVODUJPO؀नBDUJPOWBMVFGVODUJPOਸ೟ण

  71. 4BSTB ׮਺UJNFTUFQীࢲTUBUF৬BDUJPOܳل׮ࢎਊೞৈBDUJPOWBMVFܳFTUJNBUFೠ׮

  72. 4BSTBQTFVEPDPEF

  73. 4BSTBQTFVEPDPEF 0OQPMJDZ

  74. 4BSTBHSJEXPSME (PBM 4UBSU "U  4U 

  75. 4BSTBHSJEXPSME         

    ҃೷ೞ૑ঋ਷झప੉౟੄੿ࠁоহ׮

  76. 4BSTBHSJEXPSME         

    ҃೷ਸৈ۞ߣ೧ࠁݴBDUJPOWBMVFܳসؘ੉౟ೠ׮ 1PMJDZח0OQPMJDZ       

  77. 4BSTB दো

  78. 4BSTB 2MFBSOJOH Temporal-Diffrenece Learning

  79. 2MFBSOJOH 2MFBSOJOH੉ۄҊܻࠛחPGGQPMJDZ5%DPOUSPMੋ೧ъച೟ण੉ߊ੹ೞח҅ӝоغ঻׮  8BULJOT   FYQMPSBUJPOҗFYQMPJUBUJPOਸэ੉ೠ׮

  80. 2MFBSOJOHQTFVEPDPEF 0GGQPMJDZ

  81. RMFBSOJOHHSJEXPSME (PBM 4UBSU "SHNBY 4U 

  82. 2MFBSOJOHHSJEXPSME         

    ҃೷ೞ૑ঋ਷झప੉౟੄੿ࠁоহ׮

  83. 2MFBSOJOHHSJEXPSME         

    ҃೷ਸৈ۞ߣ೧ࠁݴBDUJPOWBMVFܳসؘ੉౟ೠ׮ 1PMJDZח0GGQPMJDZ       

  84. 2MFBSOJOH दো

  85. ٩۞׬೐ۨ੐ਕ௼ாۄझࣗѐगಌܻ݃য়ജ҃ҳ୷

  86. %FFQMFBSOJOH https://goo.gl/images/VA89CC

  87. %FFQMFBSOJOHਵ۽ੋ೧ https://chaosmail.github.io/deeplearning/2016/10/22/intro-to-deep-learning-for-computer-vision/ ࢎ૓ਸJOQVUਵ۽߉חѪ੉оמ೧૗

  88. %FFQ3FJOGPSDFNFOU-FBSOJOH %FFQMFBSOJOH 3FJOGPSDFNFOU-FBSOJOH https://goo.gl/images/oNu5Gr

  89. %FFQNJOE %2/ https://www.youtube.com/watch?v=V1eYniJ0Rnk %FFQMFBSOJOHਸъച೟णী੸ਊೞৈ ࢎۈࠁ׮೒ۨ੉ܳੜೞחੋҕ૑מਸ݅ٞ

  90. %2/ ,FSBT CSFBLPVU  ௏٘ࢸݺ .BJO MJCSBSZ 'VODUJPO

  91. %2/ ,FSBT CSFBLPVU  ௏٘ࢸݺ .BJO MJCSBSZ %2/

  92. ജ҃ਸࠛ۞ৡ׮ BHFOUܳࢤࢿೠ׮ TDPSF FQJTPEF HMPCBM@TUFQܳ੿೧ળ׮ ੿೧ળীೖࣗ٘݅ఀ೟णਸद੘ ݾऀ׮ࢽѐо઱য૓׮ ജ҃੄ୡӝчਸоઉৡ׮ ୊਺ੌ੿ҳрزউ߄оBDUJPOਸ੐੄۽ࢶఖೡࣻ ੓ѱೠ׮

    ਤ੄ജ҃ୡӝчਸ੉޷૑੹୊ܻ೧ળ׮ ੹୊ܻ೧ળ੉޷૑۽֎ѐ੄൤झషܻܳ݅ٚ׮ .BJO

  93. ѱ੐੉՘զٸө૑҅ࣘجѱೞחXIJMFޙਸ݅ٚ ׮ SFOEFS੄ਬޖܳഛੋೠ׮ SFOEFSਸਬޖীٮ ۄ೟णࣘبо׳ۄ૓׮ Ӗ۽ߥझచਸೞաঀט۰ળ׮ झచਸೞաঀט۰ળ׮ ֎ѐ੄TUBUF IJTUPSZ ܳ੉ਊೞৈBDUJPOਸ

    ࢶఖೠ׮ .BJO

  94. .BJO ࢶఖೠBDUJPOਵ۽ജ҃җ࢚ഐ੘ਊೞݴജ҃ীࢲ׮਺झప੉ ౟ SFXBSE EPOF JOGP੄чਸ߉ח׮ ߉਷׮਺झప੉౟ܳ׮द੹୊ܻ೧ળ׮ IJTUPSZীࢲখ੄ࣁѐ৬ߑӘ߉ইৡTUBUFܳ೤୛ࢲ OFYU@IJTUPSZ۽ࢶ঱ R@NBY੄ಣӐਸ҅࢑ೞӝਤ೧ࢲഅ੤੄NPEFM۽ࠗఠաৡ2

    ч੄NBYܳBHFOUBWH@R@NBYী؊ೠ׮ ݅ডEFBEੋ҃਋EFBEܳ5SVF۽߄ԲҊ TUBSU@MJGFܳೞա ઴ৈળ׮

  95. .BJO ੌ੿दр݃׮UBSHFUNPEFMਸVQEBUFೠ׮ ݅ডীલਵݶEFBEGBMTF۽߄ԲҊইפݶ OFYUIJTUPSZчਸIJTUPSZо߉ח׮ ݅ডীEPOF੉ݶীೖࣗ٘੄೟ण੿ࠁܳӝ۾ ೞৈ୹۱ೠ׮ ੌ੿ীೖࣗ٘݃׮ݽ؛ਸ੷੢ೠ׮ 

  96. %2/ ,FSBT CSFBLPVU  ௏٘ࢸݺ .BJO MJCSBSZ %2/

  97. .BJO ׮ܲইఋܻѱ੐ীࢲب੸ਊೡࣻ੓ب۾ܻਕ٘੄ߧਤܳ_ ۽ೠ׮ T B S Tܻܳ೒ۨ੉ݫݽܻী੷੢ೠ׮ ܻ೒ۨ੉ݫݽܻоद੘੼ࠁ׮֫ই૑ݶ೟णਸद੘ೠ׮

  98. .BJO ੌ੿दр݃׮UBSHFUNPEFMਸVQEBUFೠ׮ ݅ডীલਵݶEFBEGBMTF۽߄ԲҊইפݶ OFYUIJTUPSZчਸIJTUPSZо߉ח׮ ੌ੿ীೖࣗ٘݃׮ݽ؛ਸ੷੢ೠ׮ 

  99. *NQPSU ೙ਃೠۄ੉࠳۞ܻܳࠛ۞ৡ׮ B,FSBT $//MBZFS %FOTFMBZFS  PQUJNJ[FS  ாۄझীࢲ੄٩۞׬ݽ؛

  100. *NQPSU C੉޷૑੹୊ܻ JOQVUਵ۽ٜযয়ח੉޷૑௼ӝઑ੺ 3(#ܳ(SBZ۽݅٘חۄ੉࠳۞ܻ SFQMBZNFNPSZ݅٘ח D5FOTPSGMPX UFOTPSGMPXCBDLFOE  UFOTPSGMPX Eӝఋ

    OVNQZ  SBOEPN HZN  PT

  101. %2/ ,FSBT CSFBLPVU  ௏٘ࢸݺ .BJO MJCSBSZ %2/

  102. %2/  ↟ SFOEFS੄ਬޖ  ↟ NPEFM੄MPBEਬޖ  ↟ TUBUFࢎ੉ૉ

     ↟ BDUJPOࢎ੉ૉ  ↟ FQTJMPOч  ↟ FQTJMPO੄द੘੼җ՘੼ EFDBZܳਤ ೧   ↟ FQTJMPO੄EFDBZTUFQ੿੄  ↟ ୡӝച

  103. %2/  ↟ ܻ೒ۨ੉ݫݽܻীࢲࡳਸߓ஖ࢎ੉ૉ੿੄  ↟ ೟णਸद੘ೡӝળ੿੄  ↟ ੿׹ݽ؛۽সؘ੉౟઱ӝ੿੄

     ↟ EJTDPVOUGBDUPS  ↟ ܻ೒ۨ੉ݫݽܻ୭؀௼ӝ੿੄  ↟ झఋ౟ೡٸBDUJPOਸ੐੄۽੿೧઱חࢸ੿  ↟ %FFQMFBSOJOHNPEFM  ↟ 5BSHFUNPEFM  ↟ VQEBUFUBSHFUNPEFM  ↟ ୡӝച

  104. %2/  ↟ PQUJNJ[FS  ↟ 5FOTPSCPBSE  ↟ ୡӝച

    4BWFػݽ؛੄ਝ੉౟ܳоઉৢٸࢎਊ

  105. %2/ ,FSBT۽٩۞׬ݽ؛ٜ݅ӝ $//-BZFST %FOTF-BZFS

  106. %2/ BDUJPOਸࢶఖೞחೣࣻ QPMJDZ ৈ ӝࢲח&QTJMPOHSFFEZ അ੤NPEFM੄XFJHIUܳоઉ৬ࢲUBSHFU NPEFM੄ਝ੉౟۽সؘ੉౟ೞחೣࣻ

  107. %2/ TUBUF BDUJPO SFXBSE OFYUTUBUFܻܳ೒ۨ੉ݫݽܻী੷੢೧઱חೣࣻ 3FQMBZ.FNPSZ

  108. %2/ ܻ೒ۨ੉ݫݽܻীࢲࡳইৡߓ஖۽ݽ؛ਸ೟णೞחೣࣻ 3FQMBZNFNPSZ

  109. %2/ ↟ PQUJNJ[FS ↟ 5FOTPSCPBSE пীೖࣗ٘׼೟ण੿ࠁܳӝ۾ೞ חೣࣻ ୡӝച

  110. %2/  ↟੉޷૑੹୊ܻܳਤೠೣࣻ  ↟ ୡӝച

  111. %2/ 0QUJNJ[FSೣࣻ ৈӝࢲח)VCFS-PTTࢎਊ https://goo.gl/images/XGsfYx

  112. %2/ ,FSBT CSFBLPVU  दো

  113. गಌܻ݃য়ജ҃ҳ୷

  114. )VNBO "* %FFQMFBSOJOH 3FJOGPSDFNFOU-FBSOJOH ׮ܲبݫੋীࢲب੸ਊ੉оמೞ׮

  115. ৈ۞ജ҃ীࢎਊ ೞ૑݅ജ҃ীٮܲ੸੺ೠъച೟णঌҊ્ܻਸ੸ਊ೧ঠೠ׮

  116. ৘޹ೠъച೟ण 4UBUF௼ӝ BDUJPO੉׮ܰ׮
 
 э਷ঌҊ્ܻਸࢎਊೞ؊ۄب %FFQMFBSOJOHNPEFM IZQFSQBSBNFUFSਸ ੸੺ೞѱࢎਊ೧ঠೠ׮

  117. &NVMBUPS &OWJSPONFOU Algorithm 1SPHSBNNJOH-BOHVBHF गಌܻ݃য়ࢸ஖ী೙ਃೠ֎о૑

  118.  IUUQTXXXQZUIPOPSHEPXOMPBETWFSTJPO  IUUQTXXXBOBDPOEBDPNEPXOMPBE"OBDPOEB  IUUQTXXXUFOTPSGMPXPSHJOTUBMM5FOTPS'MPX  IUUQTLFSBTJPJOTUBMMBUJPO,FSBT 1SPHSBNNJOH-BOHVBHF1ZUIPO

  119. &NVMBUPS IUUQXXXGDFVYDPNXFCIPNFIUNM 6CVOUV TVEPBQUHFUVQEBUF TVEPBQUHFUJOTUBMMGDFVY ."$ IUUQTCSFXTIIPNFCSFXXFCTJUF 5FSNJOBMPQFOCSFXJOTUBMMGDFVY TVEPBQUHFUJOTUBMMGDFVY &NVMBUPS'$69

  120. Environment 0QFO"*@(ZN IUUQTHJUIVCDPNPQFOBJHZN QJQJOTUBMMHZN HJUDMPOFIUUQTHJUIVCDPNPQFOBJHZNHJU
 DEHZN
 QJQJOTUBMMF 0QFO"*@(ZN 0QFO"*੄(ZNਸࢎਊೞݶࠁ׮औѱъച೟णप೷੉оמೞ׮

  121. Environment #BTFMJOFT IUUQTHJUIVCDPNPQFOBJCBTFMJOFT QJQJOTUBMMCBTFMJOFT HJUDMPOFIUUQTHJUIVCDPNPQFOBJCBTFMJOFTHJU
 DECBTFMJOFT
 QJQJOTUBMMF 0QFO"*@#BTFMJOFT

  122. Environment 1IJMJQ1BRVFUUF IUUQTHJUIVCDPNQQBRVFUUFHZNTVQFSNBSJP QJQJOTUBMMHZNQVMM JNQPSUHZN JNQPSUHZN@QVMM HZN@QVMMQVMM HJUIVCDPNQQBRVFUUFHZNTVQFSNBSJP  FOWHZNNBLF

    QQBRVFUUF4VQFS.BSJP#SPTW 4VQFS.BSJP

  123. Algorithm DEEP Q-NETWORK "MHPSJUIN%2/

  124. ࢸ஖ীޙઁоࢤӟ׮ݶ  IUUQTHJUIVCDPNXPOTFPLKVOH,*14@3FJOGPSDFNFOUUSFF NBTUFS%2/
 
 য়טъച೟णपणъ੄੹ਊHJUIVCী੗ࣁೠࢸ஖ߨৢ۰֬ওणפ׮

  125. %2/ਸ੉ਊೠੋҕ૑מगಌܻ݃য়ٜ݅ӝ

  126. Ӓܻ٘ਘ٘৬যڌѱ׮ܳө 4UBUFӒܻ٘੄ઝ಴ "DUJPO࢚ ೞ ઝ ਋ 3FXBSEೣ੿ ݾ಴
 5SBOTJUJPO1SPCBCJMJUZ %JTDPVOUGBDUPS

    3FXBSE  3FXBSE  4UBUF "DUJPO Ӓܻ٘ਘ٘৬गಌܻ݃য়ജ҃

  127. (PBM੄࠺Ү (PBM 4UBSU गಌܻ݃য়חӥߊਸ੟ח੉ݾ಴ Ӓܻ٘ਘ٘੄ݾ಴חHPBMTUBUF۽оחѪ

  128. गಌܻ݃য়ীࢲ੄ജ҃ 4UBUFചݶ "DUJPO࢚ ೞ ઝ ਋ ੼೐ ׳ܻӝ BDUJPO੄ઑ೤ 3FXBSEখਵ۽੹૓ೡٸ3FXBSE

     ٍ۽оݶ
 5SBOTJUJPO1SPCBCJMJUZ %JTDPVOUGBDUPS 4UBUF "DUJPO ب଱૑ੋӥߊীоө੉тࣻ۾֫਷SFXBSEܳ߉ח׮

  129. ҅ࣘغחपಁj

  130. ੉ग ܻ݃য়оখਵ۽੹૓ೞ૑ঋਵ۰Ҋೞחഅ࢚ 4UBUFоCSFBLPVUࠁ׮؊ࠂ੟ೞҊBDUJPO੉݆׮

  131. 3FXBSEࢸ੿ ݾ಴׳ࢿೞ૑ޅೞݶ दр੉૑զٸ݃׮ ӥߊীࢲݣয૑ݶ ӥߊীоөਕ૑ݶ  ݾ಴ীب଱ೞݶ 1FOBMUZ #POVTSFXBSE୶о

  132. %FFQMFBSOJOHNPEFM 7((NPEFMBOESFHVMBS࠺Ү https://goo.gl/images/eoXooC https://goo.gl/images/s8XrCK ؊Өѱऺইࠁ੗

  133. ъച೟ण SFJOGPSDFNFOUMFBSOJOH ӝୡࢸݺ߂6OJUZNMBHFOUܳ੉ਊೞৈ݅ٚജ҃ীъച೟णঌҊ્ܻ੸ਊ  (JUIVC IUUQTHJUIVCDPNXPOTFPLKVOH 'BDFCPPL IUUQTXXXGBDFCPPLDPNXTKVOH #MPH IUUQTXPOTFPLKVOHHJUIVCJPࢿҕ

  134. %2/ਸ੉ਊೠੋҕ૑מगಌܻ݃য়ٜ݅ӝ दো

  135. ۨ߰ਸ௿ܻযೞחܻ݃য়ח݅ٚറ ׮ܲۨ߰ীࢲࢿמ੉ݒ਋ڄয૓׮0WFSGJUUJOH੉ইקө https://goo.gl/images/6uDmqH

  136. ъച೟ण੄োҳחഝߊ൤૓೯઺ 3FXBSE &YQMPSBUJPO "MHPSJUIN

  137. хࢎ೤פ׮ (JUIVC IUUQTHJUIVCDPNXPOTFPLKVOH 'BDFCPPL IUUQTXXXGBDFCPPLDPNXTKVOH #MPH IUUQTXPOTFPLKVOHHJUIVCJP

  138. 3FGFSFODFT 3FJOGPSDFNFOU-FBSOJOH"O*OUSPEVDUJPO3JDIBSE44VUUPOBOE"OESFX(#BSUP4FDPOE&EJUJPO JOQSPHSFTT.*51SFTT $BNCSJEHF  ."  IUUQTHJUIVCDPNSMDPEFSFJOGPSDFNFOUMFBSOJOHLS