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  • On the Optimality and Complexity of Reinforcement Learning

    Mc87pq726?file=thumbnail
    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