A statistical property of multiagent learning based on Markov decision process.
IEEE Trans Neural Netw
; 17(4): 829-42, 2006 Jul.
Article
em En
| MEDLINE
| ID: mdl-16856649
ABSTRACT
We exhibit an important property called the asymptotic equipartition property (AEP) on empirical sequences in an ergodic multiagent Markov decision process (MDP). Using the AEP which facilitates the analysis of multiagent learning, we give a statistical property of multiagent learning, such as reinforcement learning (RL), near the end of the learning process. We examine the effect of the conditions among the agents on the achievement of a cooperative policy in three different cases blind, visible, and communicable. Also, we derive a bound on the speed with which the empirical sequence converges to the best sequence in probability, so that the multiagent learning yields the best cooperative result.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Cadeias de Markov
/
Modelos Estatísticos
/
Aprendizagem
Tipo de estudo:
Health_economic_evaluation
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Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
IEEE Trans Neural Netw
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2006
Tipo de documento:
Article
País de afiliação:
Japão