Logarithmic Regret for Reinforcement Learning with Linear Function Approximation

Jiafan He, Dongruo Zhou, Quanquan Gu
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:4171-4180, 2021.

Abstract

Reinforcement learning (RL) with linear function approximation has received increasing attention recently. However, existing work has focused on obtaining √T-type regret bound, where T is the number of interactions with the MDP. In this paper, we show that logarithmic regret is attainable under two recently proposed linear MDP assumptions provided that there exists a positive sub-optimality gap for the optimal action-value function. More specifically, under the linear MDP assumption (Jin et al., 2020), the LSVI-UCB algorithm can achieve ˜O(d3H5/gapmin⋅log(T))regret; and under the linear mixture MDP assumption (Ayoub et al., 2020), the UCRL-VTR algorithm can achieve ˜O(d2H5/gapmin⋅log3(T)) regret, where d is the dimension of feature mapping, H is the length of episode, gapmin is the minimal sub-optimality gap, and ˜O hides all logarithmic terms except log(T). To the best of our knowledge, these are the first logarithmic regret bounds for RL with linear function approximation. We also establish gap-dependent lower bounds for the two linear MDP models.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-he21c, title = {Logarithmic Regret for Reinforcement Learning with Linear Function Approximation}, author = {He, Jiafan and Zhou, Dongruo and Gu, Quanquan}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {4171--4180}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/he21c/he21c.pdf}, url = {https://proceedings.mlr.press/v139/he21c.html}, abstract = {Reinforcement learning (RL) with linear function approximation has received increasing attention recently. However, existing work has focused on obtaining $\sqrt{T}$-type regret bound, where $T$ is the number of interactions with the MDP. In this paper, we show that logarithmic regret is attainable under two recently proposed linear MDP assumptions provided that there exists a positive sub-optimality gap for the optimal action-value function. More specifically, under the linear MDP assumption (Jin et al., 2020), the LSVI-UCB algorithm can achieve $\tilde{O}(d^{3}H^5/\text{gap}_{\text{min}}\cdot \log(T))$regret; and under the linear mixture MDP assumption (Ayoub et al., 2020), the UCRL-VTR algorithm can achieve $\tilde{O}(d^{2}H^5/\text{gap}_{\text{min}}\cdot \log^3(T))$ regret, where $d$ is the dimension of feature mapping, $H$ is the length of episode, $\text{gap}_{\text{min}}$ is the minimal sub-optimality gap, and $\tilde O$ hides all logarithmic terms except $\log(T)$. To the best of our knowledge, these are the first logarithmic regret bounds for RL with linear function approximation. We also establish gap-dependent lower bounds for the two linear MDP models.} }
Endnote
%0 Conference Paper %T Logarithmic Regret for Reinforcement Learning with Linear Function Approximation %A Jiafan He %A Dongruo Zhou %A Quanquan Gu %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-he21c %I PMLR %P 4171--4180 %U https://proceedings.mlr.press/v139/he21c.html %V 139 %X Reinforcement learning (RL) with linear function approximation has received increasing attention recently. However, existing work has focused on obtaining $\sqrt{T}$-type regret bound, where $T$ is the number of interactions with the MDP. In this paper, we show that logarithmic regret is attainable under two recently proposed linear MDP assumptions provided that there exists a positive sub-optimality gap for the optimal action-value function. More specifically, under the linear MDP assumption (Jin et al., 2020), the LSVI-UCB algorithm can achieve $\tilde{O}(d^{3}H^5/\text{gap}_{\text{min}}\cdot \log(T))$regret; and under the linear mixture MDP assumption (Ayoub et al., 2020), the UCRL-VTR algorithm can achieve $\tilde{O}(d^{2}H^5/\text{gap}_{\text{min}}\cdot \log^3(T))$ regret, where $d$ is the dimension of feature mapping, $H$ is the length of episode, $\text{gap}_{\text{min}}$ is the minimal sub-optimality gap, and $\tilde O$ hides all logarithmic terms except $\log(T)$. To the best of our knowledge, these are the first logarithmic regret bounds for RL with linear function approximation. We also establish gap-dependent lower bounds for the two linear MDP models.
APA
He, J., Zhou, D. & Gu, Q.. (2021). Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:4171-4180 Available from https://proceedings.mlr.press/v139/he21c.html.

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