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- W2024074370 abstract "This paper presents experimental results obtained with an original architecture that can do generic learning for randomly observable factored Markov decision process (ROFMDP). First, the paper describes the theoretical framework of ROFMDP and the working of this algorithm, in particular the parallelization principle and the dynamic reward allocation process. Then, the architecture is applied to two navigation problems (gridworld and New York Driving). The tests show that the architecture allows to learn a good and generic policy in spite of the large dimensions of the state spaces of both systems." @default.
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- W2024074370 date "2006-06-01" @default.
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- W2024074370 title "Apprentissage par renforcement dans le cadre des processus décisionnels de Markov factorisés observables dans le désordre. Etude expérimentale du Q-Learning parallèle appliqué aux problèmes du labyrinthe et du New York Driving" @default.
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- W2024074370 doi "https://doi.org/10.3166/ria.20.275-310" @default.
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