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Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function's In-Degree.
Doran
Chakraborty and Peter Stone.
In Proceedings of the Twenty Eighth
International Conference on Machine Learning (ICML), 2011.
[PDF]223.3kB [postscript]521.1kB
This paper introduces Learn Structure and Exploit RMax (LSE-RMax), a novel model based structure learning algorithm for ergodic factored-state MDPs. Given a planning horizon that satisfies a condition, LSE-RMax provably guarantees a return very close to the optimal return, with a high certainty, without requiring any prior knowledge of the in-degree of the transition function as input. LSE-RMax is fully implemented with a thorough analysis of its sample complexity. We also present empirical results demonstrating its effectiveness compared to prior approaches to the problem.
@InProceedings{ICML11-chakraborty,
author = "Doran Chakraborty and Peter Stone",
title="Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function's In-Degree",
booktitle="Proceedings of the Twenty Eighth International Conference on Machine Learning (ICML)",
year="2011",
abstract={This paper introduces Learn Structure and Exploit RMax (LSE-RMax), a novel model based structure learning algorithm for ergodic factored-state MDPs. Given a planning horizon that satisfies a condition, LSE-RMax provably guarantees a return very close to the optimal return, with a high certainty, without requiring any prior knowledge of the in-degree of the transition function as input. LSE-RMax is fully implemented with a thorough analysis of its sample complexity. We also present empirical results demonstrating its effectiveness compared to prior approaches to the problem.},
}
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