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On the Optimality and Complexity of Reinforcement Learning

Description:

In this dissertation, we aim to develop algorithms that achieve optimality with provable complexity guarantees under various settings in reinforcement learning (RL). Specifically, in Markov decision processes (MDPs), we study single-agent and multi-agent online RL, respectively, and offline RL under the presence of unobserved confounders. Single-agent online RL. We design...

Subject:

Industrial engineering and Computer science

Creator:

Fu, Zuyue

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

09/22/2022

Date Created:

2022-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_927346 and http://dissertations.umi.com/northwestern:16223
Methods for Nonlinear and Noisy Optimization

Description:

In this thesis, we aim to develop efficient algorithms with theoretical guarantees for noisy nonlinear optimization problems, with and without constraints, under various different assumptions. Apart from Chapter 1 which provides relevant backgrounds, the remaining of thesis is divided into four chapters. In Chapter 2, we establish the theoretical convergence...

Keyword:

LBFGS, BFGS, noisy optimization, nonlinear optimization, quasi-Newton, and derivative-free optimization

Subject:

Industrial engineering and Computer science

Creator:

Xie, Yuchen

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

10/07/2021

Date Created:

2021-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_844865 and http://dissertations.umi.com/northwestern:15749

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