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On the Statistical Complexity of Offline Policy Evaluation for Tabular Reinforcement Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	On the Statistical Complexity of Offline Policy Evaluation for Tabular Reinforcement Learning./
作者:	Yin, Ming.
面頁冊數:	1 online resource (178 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30313869click for full text (PQDT)
ISBN:	9798379496500

On the Statistical Complexity of Offline Policy Evaluation for Tabular Reinforcement Learning.
Yin, Ming.

On the Statistical Complexity of Offline Policy Evaluation for Tabular Reinforcement Learning. - 1 online resource (178 pages)

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--University of California, Santa Barbara, 2023.

Includes bibliographical references

Offline Policy Evaluation (OPE) aims at evaluating the expected cumulative reward of a target policy \uD835\uDF0B when offline data are collected by running a logging policy \uD835\uDF07. Standard importancesampling based approaches for this problem suffer from a variance that scales exponentially with time horizon \uD835\uDC3B, which motivates a splurge of recent interest in alternatives that break the "Curse of Horizon". In the Second chapter of this thesis, we prove the modification of Marginalized Importance Sampling (MIS) method can achieve the Cramer-Rao lower bound, provided that the state space and the action space are finite. In the Third chapter of the thesis, we go beyond the off-policy evaluation setting and propose a new uniform convergence for OPE. The Uniform OPE problem requires evaluating all the policies in a policy class Π simultaneously, and we obtain nearly optimal error bounds for a number of global / local policy classes. Our results imply that the model-based planning achieves an optimal episode complexity of \uD835\uDC42(\uD835\uDC3B3∕\uD835\uDC51\uD835\uDC5A \uD835\uDF16 2 ) in identifying an \uD835\uDF16-optimal policy under the timeinhomogeneous episodic MDP model. Here \uD835\uDC51\uD835\uDC5A is the minimal marginal state-action visitation probability for the current MDP under the behavior policy \uD835\uDF07. We further improve the sample complexity guarantee to \uD835\uDC42(\uD835\uDC3B2∕\uD835\uDC51\uD835\uDC5A \uD835\uDF16 2 ) under the time-homogeneous episodic MDPs, using a novel singleton-absorbing MDP technique in the Fourth chapter. Both results are known to be optimal under their respective settings. In the final part of the thesis, we summarise our work in reinforcement learning and conclude with potential future directions.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379496500Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Offline reinforcement learningIndex Terms--Genre/Form:

542853
Electronic books.

On the Statistical Complexity of Offline Policy Evaluation for Tabular Reinforcement Learning.
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504 $a Includes bibliographical references
520 $a Offline Policy Evaluation (OPE) aims at evaluating the expected cumulative reward of a target policy \uD835\uDF0B when offline data are collected by running a logging policy \uD835\uDF07. Standard importancesampling based approaches for this problem suffer from a variance that scales exponentially with time horizon \uD835\uDC3B, which motivates a splurge of recent interest in alternatives that break the "Curse of Horizon". In the Second chapter of this thesis, we prove the modification of Marginalized Importance Sampling (MIS) method can achieve the Cramer-Rao lower bound, provided that the state space and the action space are finite. In the Third chapter of the thesis, we go beyond the off-policy evaluation setting and propose a new uniform convergence for OPE. The Uniform OPE problem requires evaluating all the policies in a policy class Π simultaneously, and we obtain nearly optimal error bounds for a number of global / local policy classes. Our results imply that the model-based planning achieves an optimal episode complexity of \uD835\uDC42(\uD835\uDC3B3∕\uD835\uDC51\uD835\uDC5A \uD835\uDF16 2 ) in identifying an \uD835\uDF16-optimal policy under the timeinhomogeneous episodic MDP model. Here \uD835\uDC51\uD835\uDC5A is the minimal marginal state-action visitation probability for the current MDP under the behavior policy \uD835\uDF07. We further improve the sample complexity guarantee to \uD835\uDC42(\uD835\uDC3B2∕\uD835\uDC51\uD835\uDC5A \uD835\uDF16 2 ) under the time-homogeneous episodic MDPs, using a novel singleton-absorbing MDP technique in the Fourth chapter. Both results are known to be optimal under their respective settings. In the final part of the thesis, we summarise our work in reinforcement learning and conclude with potential future directions.
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653 $a Statistical machine learning
653 $a Policy evaluation
653 $a Target policy
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