Improved worst-case regret bounds for randomized least-squares value iteration
Agrawal, Priyank
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https://hdl.handle.net/2142/113048
Description
Title
Improved worst-case regret bounds for randomized least-squares value iteration
Author(s)
Agrawal, Priyank
Issue Date
2021-07-15
Director of Research (if dissertation) or Advisor (if thesis)
Jiang, Nan
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Reinforcement Learning
Exploration-Exploitation
Abstract
This work studies regret minimization with randomized value functions in reinforcement learning. In tabular finite-horizon Markov Decision Processes, we introduce a clipping variant of one classical Thompson Sampling (TS)-like algorithm, randomized least-squares value iteration (RLSVI). Our $\tilde{\mathrm{O}}(H^2S\sqrt{AT})$ high-probability worst-case regret bound improves the previous sharpest worst-case regret bounds for RLSVI and matches the existing state-of-the-art worst-case TS-based regret bounds.
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