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Towards Structural Generalization: Factored Approximate Planning
Zitatschlüssel Boehmer2013b
Autor Böhmer, W. and Obermayer, K.
Jahr 2013
Journal ICRA Workshop on Autonomous Learning
Zusammenfassung Autonomous agents do not always have access to the amount of samples machine learning methods require. Structural assumptions like factored MDP allow to generalize experiences beyond traditional metrics to entirely new situations. This paper introduces a novel framework to exploit such knowledge for approximated policy iteration. At the heart of the framework a novel factored approximate planning algorithm is derived. The algorithm requires no real observations and optimizes control for given linear reward and transition models. It is empirically compared with least squares policy iteration in a continuous navigation task. Computational leverage in constructing the linear models without observing the entire state space and in representation of the solution are discussed as well.
Typ der Publikation Selected:reinforcement
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