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Jiang, Nan Agrawal, Priyank 2022-01-12T21:45:44Z 2022-01-12T21:45:44Z 2021-07-15 2021-08 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. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-01-12 without embargo terms The student, Priyank Agrawal, accepted the attached license on 2021-07-14 at 20:46. The student, Priyank Agrawal, submitted this Thesis for approval on 2021-07-14 at 20:59. This Thesis was approved for publication on 2021-07-15 at 15:44. DSpace SAF Submission Ingestion Package generated from Vireo submission #16946 on 2022-01-12 at 12:45:32 Made available in DSpace on 2022-01-12T21:45:44Z (GMT). No. of bitstreams: 2 AGRAWAL-THESIS-2021.pdf: 427442 bytes, checksum: bbc5e57adc02afd39317656f59724340 (MD5) LICENSE.txt: 4212 bytes, checksum: 330f6b569f4b36aad71e7f0c0f652d33 (MD5) Previous issue date: 2021-07-15 application/pdf http://hdl.handle.net/2142/113048 en Copyright 2021 Priyank Agrawal Reinforcement Learning Exploration-Exploitation Improved worst-case regret bounds for randomized least-squares value iteration text Thesis Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.