東華大學圖書館 |

Robust Learning and Evaluation in Sequential Decision Making.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Robust Learning and Evaluation in Sequential Decision Making./
作者:	Keramati, Ramtin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	135 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Diabetes. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28688332
ISBN:	9798544203834

Robust Learning and Evaluation in Sequential Decision Making.
Keramati, Ramtin.

Robust Learning and Evaluation in Sequential Decision Making. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 135 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Reinforcement learning (RL), as a branch of artificial intelligence, is concerned with making a good sequence of decisions given experience and rewards in a stochastic environment. RL algorithms, propelled by the rise of deep learning and neural networks, have shown an impressive performance in achieving human-level performance in games like Go, Chess, and Atari. However, when applied to high-stakes real-world applications, these impressive performances are not matched. This dissertation tackles some important challenges around robustness that hinder our ability to unleash the potential of RL to real-world applications. We look at the robustness of RL algorithms in both online and online settings and introduce new algorithms that may be of particular interest when applying RL to real-world applications such as health care and education.In the first line of work, we consider an online setting where the agent can interact with the environment and collect experience and rewards to learn the optimal sequence of decisions. In many real-world applications, online interactions are limited, limiting our ability to collect data. That raises the necessity of sample efficient algorithms. In addition, safety concerns highlight the importance of learning risk-sensitive policies in these applications. We, therefore, combine recent advances in distributional reinforcement learning with the principle of optimism in the face of uncertainty to develop a scalable algorithm to learn a CVaR (conditional value at risk) optimal policy in a sample ecient manner to minimize the number of interactions needed with the environment.In high-stakes real-world applications, often any online interaction is undesirable and we have to be able to perform off-policy policy evaluation (OPE). OPE methods evaluate a new policy (evaluation policy) given the experiences collected using another policy (behavior policy). For example, an agent that aims to learn the adaptive treatment plan for patients in a hospital may not be able to collect any experiences interacting with patients but can use data containing past decisions made by clinicians and their outcomes. OPE is a counterfactual and challenging task that is often solved by making a crucial assumption, sequential ignorability. Sequential ignorability states that the evaluation policy has access to all the information used by the behavior policy to make decisions, in other words, there are no unobserved confounders. This assumption is often violated in observational data, and failure to acknowledge that results in an arbitrary biased estimate of the evaluation policy. In this dissertation, we consider the bounded e↵ect of unobserved confounders and develop a scalable algorithm to provide bounds on OPE. Our work can be used to raise concerns or certify the superior performance of an evaluation policy under the existence of unobserved confounders and prevents undesirable outcomes of deploying a new decision policy.One shortcoming of the existing OPE method for sequential decision-making is that they often evaluate the expected performance given a distribution. However, in most real-world applications, we would like to assess if the population's subgroups benefit from a newly suggested policy. In this dissertation, we take a step toward quantifying heterogeneity in OPE for sequential decision making and identify subgroups with similar benefits or harm from the evaluation policy.

ISBN: 9798544203834Subjects--Topical Terms:

544344
Diabetes.

Robust Learning and Evaluation in Sequential Decision Making.
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520 $a Reinforcement learning (RL), as a branch of artificial intelligence, is concerned with making a good sequence of decisions given experience and rewards in a stochastic environment. RL algorithms, propelled by the rise of deep learning and neural networks, have shown an impressive performance in achieving human-level performance in games like Go, Chess, and Atari. However, when applied to high-stakes real-world applications, these impressive performances are not matched. This dissertation tackles some important challenges around robustness that hinder our ability to unleash the potential of RL to real-world applications. We look at the robustness of RL algorithms in both online and online settings and introduce new algorithms that may be of particular interest when applying RL to real-world applications such as health care and education.In the first line of work, we consider an online setting where the agent can interact with the environment and collect experience and rewards to learn the optimal sequence of decisions. In many real-world applications, online interactions are limited, limiting our ability to collect data. That raises the necessity of sample efficient algorithms. In addition, safety concerns highlight the importance of learning risk-sensitive policies in these applications. We, therefore, combine recent advances in distributional reinforcement learning with the principle of optimism in the face of uncertainty to develop a scalable algorithm to learn a CVaR (conditional value at risk) optimal policy in a sample ecient manner to minimize the number of interactions needed with the environment.In high-stakes real-world applications, often any online interaction is undesirable and we have to be able to perform off-policy policy evaluation (OPE). OPE methods evaluate a new policy (evaluation policy) given the experiences collected using another policy (behavior policy). For example, an agent that aims to learn the adaptive treatment plan for patients in a hospital may not be able to collect any experiences interacting with patients but can use data containing past decisions made by clinicians and their outcomes. OPE is a counterfactual and challenging task that is often solved by making a crucial assumption, sequential ignorability. Sequential ignorability states that the evaluation policy has access to all the information used by the behavior policy to make decisions, in other words, there are no unobserved confounders. This assumption is often violated in observational data, and failure to acknowledge that results in an arbitrary biased estimate of the evaluation policy. In this dissertation, we consider the bounded e↵ect of unobserved confounders and develop a scalable algorithm to provide bounds on OPE. Our work can be used to raise concerns or certify the superior performance of an evaluation policy under the existence of unobserved confounders and prevents undesirable outcomes of deploying a new decision policy.One shortcoming of the existing OPE method for sequential decision-making is that they often evaluate the expected performance given a distribution. However, in most real-world applications, we would like to assess if the population's subgroups benefit from a newly suggested policy. In this dissertation, we take a step toward quantifying heterogeneity in OPE for sequential decision making and identify subgroups with similar benefits or harm from the evaluation policy.
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