InforLorV4, PascalFrancis, Corpus, bibRecord, 000271

The factored policy-gradient planner

Identifieur interne : 000271 ( PascalFrancis/Corpus ); précédent : 000270; suivant : 000272

The factored policy-gradient planner

Auteurs : Olivier Buffet ; Douglas Aberdeen

Source :

Artificial intelligence [ 0004-3702 ] ; 2009.

RBID : Pascal:09-0228244

Descripteurs français

Pascal (Inist)
- Planification, Apprentissage renforcé, Métrique, Approche probabiliste, Modélisation, Fonction discrète, Programmation dynamique, Programmation stochastique, Recherche locale, Approximation d'une fonction.

English descriptors

KwdEn :
- Discrete function, Dynamic programming, Function approximation, Local search, Metric, Modeling, Planning, Probabilistic approach, Reinforcement learning, Stochastic programming.

Abstract

We present an any-time concurrent probabilistic temporal planner (CPTP) that includes continuous and discrete uncertainties and metric functions. Rather than relying on dynamic programming our approach builds on methods from stochastic local policy search. That is, we optimise a parameterised policy using gradient ascent. The flexibility of this policy-gradient approach, combined with its low memory use, the use of function approximation methods and factorisation of the policy, allow us to tackle complex domains. This factored policy gradient (FPG) planner can optimise steps to goal, the probability of success, or attempt a combination of both. We compare the FPG planner to other planners on CPTP domains, and on simpler but better studied non-concurrent non-temporal probabilistic planning (PP) domains. We present FPG-IPC, the PP version of the planner which has been successful in the probabilistic track of the fifth international planning competition.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A12	`01`	`1`		`@1 FOX (Maria) @9 ed.`
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Format Inist (serveur)

NO :	PASCAL 09-0228244 INIST
ET :	The factored policy-gradient planner
AU :	BUFFET (Olivier); ABERDEEN (Douglas); FOX (Maria); THIEBAUX (Sylvie)
AF :	LORIA-INRIA, Nancy University/Nancy/France (1 aut.); Google Inc/Zurich/Suisse (2 aut.); Department of Computer and Information Sciences, University of Strathclyde/Glasgow/Royaume-Uni (1 aut.); Computer Sciences Laboratory & NICTA, Australian National University/Canberra/Australie (2 aut.)
DT :	Publication en série; Niveau analytique
SO :	Artificial intelligence; ISSN 0004-3702; Coden AINTBB; Pays-Bas; Da. 2009; Vol. 173; No. 5-6; Pp. 722-747; Bibl. 53 ref.
LA :	Anglais
EA :	We present an any-time concurrent probabilistic temporal planner (CPTP) that includes continuous and discrete uncertainties and metric functions. Rather than relying on dynamic programming our approach builds on methods from stochastic local policy search. That is, we optimise a parameterised policy using gradient ascent. The flexibility of this policy-gradient approach, combined with its low memory use, the use of function approximation methods and factorisation of the policy, allow us to tackle complex domains. This factored policy gradient (FPG) planner can optimise steps to goal, the probability of success, or attempt a combination of both. We compare the FPG planner to other planners on CPTP domains, and on simpler but better studied non-concurrent non-temporal probabilistic planning (PP) domains. We present FPG-IPC, the PP version of the planner which has been successful in the probabilistic track of the fifth international planning competition.
CC :	001D02A05; 001D02C
FD :	Planification; Apprentissage renforcé; Métrique; Approche probabiliste; Modélisation; Fonction discrète; Programmation dynamique; Programmation stochastique; Recherche locale; Approximation d'une fonction
ED :	Planning; Reinforcement learning; Metric; Probabilistic approach; Modeling; Discrete function; Dynamic programming; Stochastic programming; Local search; Function approximation
SD :	Planificación; Aprendizaje reforzado; Métrico; Enfoque probabilista; Modelización; Función discreta; Programación dinámica; Programación estocástica; Busca local; Aproximación de funciones
LO :	INIST-15159.354000184847340080
ID :	09-0228244

Links to Exploration step

Pascal:09-0228244

Le document en format XML

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	Serveur d'exploration sur la recherche en informatique en Lorraine
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Serveur d'exploration sur la recherche en informatique en Lorraine

The factored policy-gradient planner

The factored policy-gradient planner

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