Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function’s In-Degree

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》资源》论文》Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function’s In-Degree

Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function’s In-Degree

2020-02-27 |

61 |

34 |

Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function’s In-Degree
论文

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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