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Reinforcement Learning from classic to DQN

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Avatar for Wonseok Jung Wonseok Jung
September 13, 2018
Research
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Reinforcement Learning from classic to DQN

고전강화학습부터 DQN 까지 설명 자료 입니다.

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Wonseok Jung

September 13, 2018
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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