Random sampling of states in dynamic programming.
IEEE Trans Syst Man Cybern B Cybern
; 38(4): 924-9, 2008 Aug.
Article
en En
| MEDLINE
| ID: mdl-18632379
ABSTRACT
We combine three threads of research on approximate dynamic programming sparse random sampling of states, value function and policy approximation using local models, and using local trajectory optimizers to globally optimize a policy and associated value function. Our focus is on finding steady-state policies for deterministic time-invariant discrete time control problems with continuous states and actions often found in robotics. In this paper, we describe our approach and provide initial results on several simulated robotics problems.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Programación Lineal
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Teoría de Sistemas
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Robótica
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Modelos Estadísticos
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Dinámicas no Lineales
Tipo de estudio:
Clinical_trials
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Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
IEEE Trans Syst Man Cybern B Cybern
Asunto de la revista:
ENGENHARIA BIOMEDICA
Año:
2008
Tipo del documento:
Article