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Equi-Gradient Temporal Difference Learning

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(1)Equi-Gradient Temporal Difference Learning Manuel Loth, Manuel Davy, Rémi Coulom, Philippe Preux. To cite this version: Manuel Loth, Manuel Davy, Rémi Coulom, Philippe Preux. Equi-Gradient Temporal Difference Learning. Kernel Methods and Reinforcement Learning, workshop of ICML 2006, Jun 2006, Pittsburgh, USA, United States. �inria-00117178�. HAL Id: inria-00117178 https://hal.inria.fr/inria-00117178 Submitted on 30 Nov 2006. HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés..

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(122) H. ΦA T res(k+1). = ΦA T res(k). +. T. T. −αc ΦA ΦA (ΦA ΦA ). −1. sgn. = c sgn − αc sgn = (1 − α)c sgn. ∗. ∗. i. *Ç ² €n~X9ƒˆŠz~XˆZx‘»´ xy² €ˆŠX±»z~Vxy²3ºl~U{!xy² ˆŠxÌ~V>ˆ†(x~VƒZ€n´¡€@ˆxw3z€@{ ˆzy€ÀˆV¶*ˆ¼·R{oX€@{{†‚»zzy€@XˆŠxy€@„jxy»¢x²3€Àz€@{y~X„Rw>ˆŠ‘x² ˆŠ%ˆ†xy~Xƒ€ »Z3€@{@ÆnpR»xy²3€ƒˆVw3€#»Š´ α ¶~XV7½>€#†‰² »Z{y€‚Äˆ{*x²3€#{yºÀˆŠXV€Á{cx {yw †‰²Äx² ˆx

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(125) dŸ{•€@€b7~X± Æ 9q(Æ H. E. H9YyB5’>N5TXWB9N5T¡MED. Ç*² »w3±Z²×~VxÀºˆ¼·³3»Šxӈо ¾>€ÁˆŠzÀ†X€@ˆzyX·%~VÃx²3€ÄˆŠX±»z~Vxy²3º¢| x²3€C¶n€‚~X±²\x<»ZÓˆŠÓˆZ†(xy~Xƒ€*´¡€Áˆxw3zy€oºÀˆ¼· Å z‰{cx,~V †‚zy€Áˆ{y€|\ˆŠ „ x²3€‚Ã„R€@†‚zy€Áˆ{y€|̈Z{x²3€X€@ˆZ{cxyuv{‡\w ˆŠz€@{#„R~Vz€@†xy~X»Ã†‰² ˆŠ3±Z€@{ ´¡z»º&{•xy€@¾µx»{•xy€@¾GÆÇ*²3~U{o~XXVw {•xyz‰ˆx€@{Cxy²3€»Z¾Rxy~XºÀˆŠ!w {•€É»Š´ ¶n€‚~X±²\x‰{ ~X¿x²3€ Å z{•x{cx€‚¾G|9x²3€

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(128) ´gˆZ†(xx² ˆxo{y± ÆnpR»À¶²3€‚ ~VxÀ†‰² ˆŠ3±Z€@{„3w3zy~X3± ˆj{•xy€‚¾E|‘~L(βxÀ{•² ) »=w3U∂|β „%½K€|/∂β z€‚º»ƒ€Á„)´¡zy»Zº x²3€ˆ†(x~VƒZ€{y€x¤ˆ´[xy€@zo²>ˆ¼ƒ\~X3±Àx‰ˆŠ¹Z€‚jˆ{cx€‚¾j½>ˆ†‰¹Ä{•»ˆ{Cxy» {•xyz~U†(xyX· @€‚z»É~Vx@| ½\·¿V€‚x•xy~X3± H. £6 F. Φ2. i. (1). −α∗ u. Ž. @. j. Φ1. ?. ñ Éÿ 9õÿnñòô\õ ÿcñòý¼ïþUï‰õ‚ÿ Zîcï Áïÿ nõ nó ‰ø@îœîcï‰ðLõÿcï(üÉαÿcø”ÿ \ïnô\ï÷šîcï œñLü \õ@ð>õ <õ yÿcñLýÁï*þUï(õφÿ Zîcï φ ú .  

(129)  . ∗. %  &, . . .  &. &.  . . j. H. α0 =.   2&,. j. −βj Φj u. aœ>{cx€@ˆ„»Š´3»xy~X3±Àˆ´[xy€@zy¶*ˆŠz‰„3{,xy²>ˆxCˆ†‰² ˆŠ ±€”»Š´‘{•~X±»R†(u †‚w3z€@„Óˆ „ÀºÀˆŠ¹9~X3±x²3~X{*{•xy€@¾¿½ ˆZ†‰¹K| {y²3»Zw3X„Ó½>€¤†‰²3»Z{y€‚ ˆZ{nxy²3€{yºÀˆŠXV€Á{cx*x² ˆx¶n€xy² €‚zˆ†xy~Xƒˆαxy€Á{ˆÉ3€‚¶Ö´¡€@ˆŠxyw3z€¤»z @€‚z»9€@{,xy²3€

(130) ¶n€‚~X±²\x»Š´‘ˆŠ¢ˆ†xy~Xƒ€”´¡€@ˆŠxyw3z€Æ t´[xy€@zo~Vx{”„R€uœˆ†xy~Xƒˆxy~X»E|Kˆ´¡€Áˆxw3zy€É{•² »w3U„¢zy€@ºÀˆŠ~Xµˆ¼ƒ¼ˆ~VVu ˆ½3X€o´¡»ZzˆÉ¾>»\{y{y~V½ V€

(131) z€uœˆ†xy~Xƒ¼ˆŠxy~X»GÆ M7_5ABEI\T B9N5T¡MED t 3»zºÀˆŠX~ Áˆx~V»Z%»Š´xy² €ƒˆŠz~UˆŠ½3X€@{ Á´¡€Áˆxyw zy€Á{¤~X{~Vº¾K»Z{y€@„ ~Xd¡ËÌ´¡zy»Z%€x ˆŠÚÆX|͍΍Π!“”w3~X±w €|Ì͊ÎZÎZύqÆÓavx†‚» {y~X{•x{~V ˆZ{y{y€‚zyxy~X3±xy² ˆŠx. 2. &. 1. ∗. α∗ = arg min ∃i ∈ / A, |ΦT i resk+1 (α)| = (1 − α)c. Ø €

(132) ² ˆ¼ƒZ€. α∈[0,1]. =H. . |ΦT i resk+1 (α)| = (1 − α)c ⇔ ⇔. ⇔. tC „¢{•» α∗ =. º~V.

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(146) +. {. G?. E@. |ΦT i (res(k) − αu)| = (1 − α)c T ΦT i res(k) − αΦi u = (1 − α)c or T −ΦT i res(k) + αΦi u = (1 − α)c. α= or α=. kΦ1 k2 ' kΦ2 k2 ' . . . sX kΦi k2 = φi (xt )2. c−ΦT i res(k) c−ΦT iu. ¶ ~Vxy² avx,~X{‘~Xº¾>»Zz•x‰ˆŠ\x‘~Vx²3€~U„R€@ˆ¤»´ Þ:9VÞ èÚá?>ç¶²3€‚z€*»3€X»9»¹R{ ´¡»Zznˆzˆ3¹9~V3±#»Š´5xy² €oƒˆŠz~UˆŠ½3X€@{Áé ´¡€Áˆê xyw zy€Á{‚|н wRxn »Šxn†‚zyw>†~UˆŠ ~Xxy²3€

(147) ~U„R€Áˆ»´!±ZzˆZ„R~X€‚\x*„3€@{†€‚\xÁÆ Ç*² € †z~Lx€‚z~V»Z.»Š´e¾3z» Å x‰ˆŠ½3~XV~Vxc· dŸ†»Zzyz€‚Uˆx~V»Z>q „R€ Å 3€@„ ˆ½>»ƒZ€|7½>€@~V3±µˆ¢±ZzˆZ„R~X€‚\x

(148) ¶z•xÁÆÓxy² €Ó¶,€@~V±Z²\x{{y€‚X€@†x{¤´¡€Áˆu xw3z€@{x² ˆx̽K»Šx² •±» n~XÀˆ¤±»9»R„„R~Xz€@†(x~V»ZÄd¡±€@»º€xzy~U†‚ˆVX· t. c+ΦT i res(k) c+ΦT iu. T c − ΦT i res(k) c + Φi res(k) ; } φi c − ΦT c + ΦT iu iu. ?.   

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(155) F. Φ2 Φ1. (1).  

(156)   Zï Zîvÿ vÿcï œï‰ðòïÿ φ1 Rï(õ&,œï#ÿ\ï#ô\ø@îcó ø@þ ñ% ‚îcï(õ‚ÿcïîõ‚ô9ü &Zÿœÿcñòô,)õ œóõ‚ðòðC÷!ï‰ñ$

(157) ÿÓøÁô×ñòÿÓ÷Ìñòðòð2Rï Φ1 ó

(158) ø@îcïRï‰ôZï \ ÿ•õ\ðòïÉÿ\õ@ô ø@ô φ2 ú  \ï œï‰øÁô\üõ@ô\ü)ðLõvÿ vÿcï. ÷ÌñòðòðKÿ•õ@ù¼ï β ÿcø 0 3õ‚ô9ü @ñLýÁï fb(x) = β φ (x) õ →Ìñ ÿ resZø ZðV=ü3ú 0 ´¡»ZzyºÐˆ{•ºÀˆŠXPˆŠ3±ZV€o¶~Lx²Óxy² €oz€@{y~X„3w ˆŠ[q̈ „À² ˆ¼ƒZ€Cˆ#UˆŠz±€  »zºÆÇ*²3€ozy€Áˆ{y»~X{Ìxy² ˆŠx<x²3€C3»ZzyºÀ{‘»´5xy²3€o¶,€@~V±Z²\x{̈zy€ ¾K€‚>ˆŠX~ @€@„5|‘ˆŠ>„%ˆ¢´¡€@ˆŠxyw3z€Ó¶~Vxy²³ˆÄUˆŠz±€@z

(159) 3»zº z€@‡\w3~Xz€@{ X€@{{”¶,€@~V±Z²\x”x»¾ zy»R„Rw †‚€ÀˆŠ €ÊP€@†(x

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