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꿈꾸는 Agent

Wonseok Jung
September 28, 2018

꿈꾸는 Agent

Wonseok Jung

September 28, 2018
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  1. 
 8POTFPL+VOH $JUZ6OJWFSTJUZPG/FX:PSL#BSVDI$PMMFHF %BUB4DJFODF.BKPS  $POOFYJPO"*'PVOEFS %FFQ-FBSOJOH$PMMFHF3FJOGPSDFNFOU-FBSOJOH3FTFBSDIFS .PEVMBCT$53--FBEFS 4DIPPMPG"*0QFSBUPS
 3FJOGPSDFNFOU-FBSOJOH

    0CKFDU%FUFDUJPO $IBUCPU (JUIVC IUUQTHJUIVCDPNXPOTFPLKVOH 'BDFCPPL IUUQTXXXGBDFCPPLDPNXTKVOH
 #MPH IUUQTXPOTFPLKVOHHJUIVCJP
 :PVUVCF
 IUUQTXXXZPVUVCFDPNDIBOOFM6$N5Y8,EIM8W+6GS3X

  2. 
 1SFTFOUBUJPOBOE-FDUVSFT  5FOTPSGMPX,33FJOGPSDFNFOU-FBSOJOH ऱ੉Ӓېݠ%*:ъച೟ण ա݅੄गಌܻ݃য়ڣٯڣٯٜ݅ӝ  IUUQTXXXZPVUVCFDPNXBUDI W#.*JSCW2X 

    ী੉੹౟ܳࠗఌ೧5IFSJTFPG3FJOGPSDFNFOU-FBSOJOH IUUQTXXXZPVUVCFDPNXBUDI WVI9V"L3G.2 TUೣԋೞח٩۞׬ஶಌ۠झ3VOOJOH"* IUUQTXXXTMJEFTIBSFOFUXPOTFPLKVOHEFFQMFBSOJOHDPOG ౵੉௑௏ܻইੋҕ૑מगಌܻ݃য়੄Ѣ੄ݽٚѪ IUUQTTQFBLFSEFDLDPNXPOTFPLKVOHBEPUJTVQFSNBSJP XJUISFJOGPSDFNFOUMFBSOJOH  ೠҴ੿ࠁ୊ܻ೟ഥъച೟ण੉ۿҗपઁ IUUQTXXXTMJEFTIBSFOFUXPOTFPLKVOHSM ా҅աۄъച೟ण੉ۿҗपઁ IUUQTXXXTMJEFTIBSFOFUXPOTFPLKVOHSM
  3. ALL ANIMALS HAVE THE ABILITY TO LEARN - ݽٚزޛ਷೟णמ۱੉੓׮ -

    ৈѐ੄न҃ࣁನ݅ਸыҊ੓ח৘ࢂԘ݃ࢶ୽ژೠ೟णמ۱੉੓׮ - ݠ୍ܻࣻ߈ࢎIFBEXJUIESBXTSFGMFYਤ೷ೠޛ୓о੓ਸѪ੉ۄ౸ױীٮܲ߈ࢎ೯ز - ৘ࢂԘ݃ࢶ୽੄ݠܻܳѤܻ٘ݶੌ੿Ѣٍܻܳ۽р׮ HOW ANIMALS LEARN
  4. LAW OF EFFECT - &EXBSE5IPSOEJLF   - -BXPGFGGFDUযڃ೯ز੄Ѿҗо݅઒झ۞਋ݶ׮਺ীبӒ೯زਸ߈ࠂೠ׮ ߈؀۽݅઒ೞ૑ঋਵݶӒ೯زਸೞ૑ঋח׮

    - 3FJOGPSDFNFOU ъച ੉੹ীੌযդ೯زਸ߈ࠂೞѱ݅٘ח੗ӓ - 1VOJTINFOU ୊ߥ ੉੹ীੌযդ೯زਸೖೞѱ݅٘ח੗ӓ HOW ANIMALS LEARN
  5. LEARNING 3FJOGPSDFNFOUMFBSOJOH਷3FXBSE ࠁ࢚ ਸ୭؀ചೞחBDUJPO ೯ز ਸࢶఖೠ׮ -FBSOFS ߓ਋ח੗ חৈ۞BDUJPOਸ೧ࠁݴ SFXBSEܳо੢֫ѱ߉חBDUJPOਸ଺ח׮

    ࢶఖػBDUJPO੉׼੢੄SFXBSEࡺ݅ইצ ׮਺੄࢚ടژח׮਺ੌযաѱؼ SFXBSEীب৔ೱਸՙசࣻب੓׮ "DUJPO ׼੢੄ ࢚ട߸ച ޷ې੄࢚ട 3FXBSE ޷ې੄3FXBSE REINFORCEMENT LEARNING
  6. MARKOV DECISION PROCESS "DUJPO "HFOU &OWJSPONFOU 3FXBSE A t R

    t 4UBUF S t R t+1 S t+1 REINFORCEMENT LEARNING
  7. AGENT "DUJPO "HFOU &OWJSPONFOU 3FXBSE A t R t 4UBUF

    S t R t+1 S t+1 REINFORCEMENT LEARNING
  8. ACTION "DUJPO "HFOU &OWJSPONFOU 3FXBSE A t R t 4UBUF

    S t R t+1 S t+1 REINFORCEMENT LEARNING
  9. OBSERVATION, REWARD "DUJPO "HFOU &OWJSPONFOU 3FXBSE A t R t

    4UBUF S t R t+1 S t+1 REINFORCEMENT LEARNING
  10. MARKOV DECISION PROCESS &OWJSPONFOU 3FXBSE A t R t S

    t R t+1 S t+1 REINFORCEMENT LEARNING &TDBQFGSPN UIFCPY 1PTJUJWF3FXBSE

  11. MARKOV DECISION PROCESS "DUJPO "HFOU &OWJSPONFOU 3FXBSE A t R

    t 4UBUF S t R t+1 S t+1 SUPERMARIO WITH R.L 3FXBSE  1FOBMUZ
  12. DOUBLE DQN SUPERMARIO WITH R.L JOQVU "DUJPO WBMVF &OW 2/FUXPSL

    s’ s 3FQMBZNFNPSZ 2 T B a r (S t , A t , R t+ 1 , S t+ 1 )
  13. HOW WE CAN ALLOW OUT A.I SYSTEM MAKE TO USE

    PRIOR KNOWLEDGE? REINFORCEMENT LEARNING https://ubisafe.org/explore/demeanure-clipart-prior-knowledge/
  14. 2. META-LEARNING META LEARNING Walking the street Get the flag

    Go to the target Hit the ball Clear the game Kick the target Find the red ball
  15. WONSEOK JUNG ߊݺо ݅ചо җ೟੗ Ѥ୷о оࣻ Ѻైӝࢶࣻ ੗زର٣੗੉ց ઁಿ٣੗੉ց

    ੘ҋо Է ఋై੉झ౟ ࣿ૘਍৔ ஠ۨ੉ࢲ झ௢ఠ׮੉ߡ ੘о ಁ࣌٣੗੉ց ೾झ౟ۨ੉ց Үਭо ҃৔ஶࢸఢ౟ ؘ੉ఠࢎ੉঱౭झ౟ ੋҕ૑מোҳਗ Үਭо ੋҕ૑מূ૑פয ۽ࠈੋҕ૑מোҳਗ
  16. 2018 QuantNet Rankings of Best Financial Engineering Programs Ranked by

    QuantNet in Dec 2017. The most comprehensive 2018 ranking of best Financial Engineering (MFE), Mathematical Finance programs in North America. The 2018 QuantNet ranking of Financial Engineering/Quantitative Finance masters programs in North America provides detailed information on placement and admission statistics from top programs the region, making it uniquely valuable to the quant finance community at large. The 2018 QuantNet rankings are best positioned to help prospective applicants decide where to apply and enroll in those master quantitative programs. 2018 Financial Engineering Programs Rankings Methodology Share your opinion about the 2018 QuantNet ranking. Rank Program Total score Peer assessment score Employment rate at graduation Employment rate 3 months after graduation Average starting base salary (not including bonus) Average GRE Quant of admitted students Tuition Class Size 1 Baruch College, City University of New York Financial Engineering New York, NY 100 4.1 94% 100% $110,000 169.5 $40,980 (non- resident), $27,675 (resident) 36FT, 4PT 1 University of California, Berkeley Financial Engineering Berkeley, CA 100 4.2 84% 96% $108,286 168 $68,725 68FT 3 Carnegie Mellon University Computational Finance Pittsburgh, PA 98 4.3 68% 85% $100,939 169 $83,100 97FT 3 Columbia University Financial Engineering New York, NY 98 3.9 97% 100% $93,000 169 $69,696 99FT
  17. WONSEOK JUNG ߊݺо ݅ചо җ೟੗ Ѥ୷о оࣻ Ѻైӝࢶࣻ ੗زର٣੗੉ց ઁಿ٣੗੉ց

    ੘ҋо Է ఋై੉झ౟ ࣿ૘਍৔ ஠ۨ੉ࢲ झ௢ఠ׮੉ߡ ੘о ಁ࣌٣੗੉ց ೾झ౟ۨ੉ց Үਭо ҃৔ஶࢸఢ౟ ؘ੉ఠࢎ੉঱౭झ౟ ੋҕ૑מোҳਗ Үਭо ੋҕ૑מূ૑פয ۽ࠈੋҕ૑מোҳਗ
  18. WONSEOK JUNG ߊݺо ݅ചо җ೟੗ Ѥ୷о оࣻ Ѻైӝࢶࣻ ੗زର٣੗੉ց ઁಿ٣੗੉ց

    ੘ҋо Է ఋై੉झ౟ ࣿ૘਍৔ ஠ۨ੉ࢲ झ௢ఠ׮੉ߡ ੘о ಁ࣌٣੗੉ց ೾झ౟ۨ੉ց Үਭо ҃৔ஶࢸఢ౟ ؘ੉ఠࢎ੉঱౭झ౟ ੋҕ૑מোҳਗ Үਭо ੋҕ૑מূ૑פয ۽ࠈੋҕ૑מোҳਗ
  19. ೞҊर਷ োҳ઱ઁо ੓׮ݶ ־ҳٚ૑ োҳपਸ ٜ݅ ࣻ ੓Ҋ ੤߀য ࠁ੉ח

    োҳपਸ ଺ও׮ݶ ־ҳա োҳपী ଵৈೡ ࣻ ੓ח ݽف ݽৈ ೣԋ োҳೞח ࣁ࢚ী হ؍ ױ ೞա੄ ৌܽ োҳࣗ э੉ೡࣻب੓যਃ