Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration
DOI:
https://doi.org/10.1609/aaai.v35i8.16813Keywords:
Reinforcement Learning, Online Learning & BanditsAbstract
This paper 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.Downloads
Published
2021-05-18
How to Cite
Agrawal, P., Chen, J., & Jiang, N. (2021). Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 6566-6573. https://doi.org/10.1609/aaai.v35i8.16813
Issue
Section
AAAI Technical Track on Machine Learning I