A Finite Sample Complexity Bound for Distributionally Robust Q-learning

Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:3370-3398, 2023.

Abstract

We consider a reinforcement learning setting in which the deployment environment is different from the training environment. Applying a robust Markov decision processes formulation, we extend the distributionally robust Q-learning framework studied in [Liu et. al. 2022]. Further, we improve the design and analysis of their multi-level Monte Carlo estimator. Assuming access to a simulator, we prove that the worst-case expected sample complexity of our algorithm to learn the optimal robust Q-function within an $\epsilon$ error in the sup norm is upper bounded by $\tilde O(|S||A|(1-\gamma)^{-5}\epsilon^{-2}p_{\wedge}^{-6}\delta^{-4})$, where $\gamma$ is the discount rate, $p_{\wedge}$ is the non-zero minimal support probability of the transition kernels and $\delta$ is the uncertainty size. This is the first sample complexity result for the model-free robust RL problem. Simulation studies further validate our theoretical results.

Cite this Paper

BibTeX
@InProceedings{pmlr-v206-wang23b, title = {A Finite Sample Complexity Bound for Distributionally Robust Q-learning}, author = {Wang, Shengbo and Si, Nian and Blanchet, Jose and Zhou, Zhengyuan}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {3370--3398}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/wang23b/wang23b.pdf}, url = {https://proceedings.mlr.press/v206/wang23b.html}, abstract = {We consider a reinforcement learning setting in which the deployment environment is different from the training environment. Applying a robust Markov decision processes formulation, we extend the distributionally robust Q-learning framework studied in [Liu et. al. 2022]. Further, we improve the design and analysis of their multi-level Monte Carlo estimator. Assuming access to a simulator, we prove that the worst-case expected sample complexity of our algorithm to learn the optimal robust Q-function within an $\epsilon$ error in the sup norm is upper bounded by $\tilde O(|S||A|(1-\gamma)^{-5}\epsilon^{-2}p_{\wedge}^{-6}\delta^{-4})$, where $\gamma$ is the discount rate, $p_{\wedge}$ is the non-zero minimal support probability of the transition kernels and $\delta$ is the uncertainty size. This is the first sample complexity result for the model-free robust RL problem. Simulation studies further validate our theoretical results.} }
Endnote
%0 Conference Paper %T A Finite Sample Complexity Bound for Distributionally Robust Q-learning %A Shengbo Wang %A Nian Si %A Jose Blanchet %A Zhengyuan Zhou %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-wang23b %I PMLR %P 3370--3398 %U https://proceedings.mlr.press/v206/wang23b.html %V 206 %X We consider a reinforcement learning setting in which the deployment environment is different from the training environment. Applying a robust Markov decision processes formulation, we extend the distributionally robust Q-learning framework studied in [Liu et. al. 2022]. Further, we improve the design and analysis of their multi-level Monte Carlo estimator. Assuming access to a simulator, we prove that the worst-case expected sample complexity of our algorithm to learn the optimal robust Q-function within an $\epsilon$ error in the sup norm is upper bounded by $\tilde O(|S||A|(1-\gamma)^{-5}\epsilon^{-2}p_{\wedge}^{-6}\delta^{-4})$, where $\gamma$ is the discount rate, $p_{\wedge}$ is the non-zero minimal support probability of the transition kernels and $\delta$ is the uncertainty size. This is the first sample complexity result for the model-free robust RL problem. Simulation studies further validate our theoretical results.
APA
Wang, S., Si, N., Blanchet, J. & Zhou, Z.. (2023). A Finite Sample Complexity Bound for Distributionally Robust Q-learning. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:3370-3398 Available from https://proceedings.mlr.press/v206/wang23b.html.

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