Bayesian Learning of Recursively Factored Environments. Bellemare, M, Veness, J, & Bowling, M webdocs.cs.ualberta.ca. abstract bibtex Abstract Model-based reinforcement learning techniques have historically encountered a number of difficulties scaling up to large observation spaces. One promising approach has been to decompose the model learning task into a number of smaller, more manageable.
@Article{Bellemare,
author = {Bellemare, M and Veness, J and Bowling, M},
title = {Bayesian Learning of Recursively Factored Environments},
journal = {webdocs.cs.ualberta.ca},
volume = {},
number = {},
pages = {},
year = {},
abstract = {Abstract Model-based reinforcement learning techniques have historically encountered a number of difficulties scaling up to large observation spaces. One promising approach has been to decompose the model learning task into a number of smaller, more manageable.},
location = {},
keywords = {}}
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