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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 andExploit RMax (LSE-RMax), a novel modelbased structure learning algorithm for ergodicfactored-state MDPs. Given a planninghorizon that satisfies a condition, LSE-RMaxprovably guarantees a return very close tothe optimal return, with a high certainty,without requiring any prior knowledge of thein-degree of the transition function as input.LSE-RMax is fully implemented witha thorough analysis of its sample complexity.We also present empirical results demonstratingits effectiveness compared to priorapproaches 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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