A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs

Kihyuk Hong, Ambuj Tewari
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:18711-18737, 2024.

Abstract

We study offline reinforcement learning (RL) with linear MDPs under the infinite-horizon discounted setting which aims to learn a policy that maximizes the expected discounted cumulative reward using a pre-collected dataset. Existing algorithms for this setting either require a uniform data coverage assumptions or are computationally inefficient for finding an ϵ-optimal policy with O(ϵ2) sample complexity. In this paper, we propose a primal dual algorithm for offline RL with linear MDPs in the infinite-horizon discounted setting. Our algorithm is the first computationally efficient algorithm in this setting that achieves sample complexity of O(ϵ2) with partial data coverage assumption. Our work is an improvement upon a recent work that requires O(ϵ4) samples. Moreover, we extend our algorithm to work in the offline constrained RL setting that enforces constraints on additional reward signals.

Cite this Paper


BibTeX
@InProceedings{pmlr-v235-hong24e, title = {A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear {MDP}s}, author = {Hong, Kihyuk and Tewari, Ambuj}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {18711--18737}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/hong24e/hong24e.pdf}, url = {https://proceedings.mlr.press/v235/hong24e.html}, abstract = {We study offline reinforcement learning (RL) with linear MDPs under the infinite-horizon discounted setting which aims to learn a policy that maximizes the expected discounted cumulative reward using a pre-collected dataset. Existing algorithms for this setting either require a uniform data coverage assumptions or are computationally inefficient for finding an $\epsilon$-optimal policy with $\mathcal{O}(\epsilon^{-2})$ sample complexity. In this paper, we propose a primal dual algorithm for offline RL with linear MDPs in the infinite-horizon discounted setting. Our algorithm is the first computationally efficient algorithm in this setting that achieves sample complexity of $\mathcal{O}(\epsilon^{-2})$ with partial data coverage assumption. Our work is an improvement upon a recent work that requires $\mathcal{O}(\epsilon^{-4})$ samples. Moreover, we extend our algorithm to work in the offline constrained RL setting that enforces constraints on additional reward signals.} }
Endnote
%0 Conference Paper %T A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs %A Kihyuk Hong %A Ambuj Tewari %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-hong24e %I PMLR %P 18711--18737 %U https://proceedings.mlr.press/v235/hong24e.html %V 235 %X We study offline reinforcement learning (RL) with linear MDPs under the infinite-horizon discounted setting which aims to learn a policy that maximizes the expected discounted cumulative reward using a pre-collected dataset. Existing algorithms for this setting either require a uniform data coverage assumptions or are computationally inefficient for finding an $\epsilon$-optimal policy with $\mathcal{O}(\epsilon^{-2})$ sample complexity. In this paper, we propose a primal dual algorithm for offline RL with linear MDPs in the infinite-horizon discounted setting. Our algorithm is the first computationally efficient algorithm in this setting that achieves sample complexity of $\mathcal{O}(\epsilon^{-2})$ with partial data coverage assumption. Our work is an improvement upon a recent work that requires $\mathcal{O}(\epsilon^{-4})$ samples. Moreover, we extend our algorithm to work in the offline constrained RL setting that enforces constraints on additional reward signals.
APA
Hong, K. & Tewari, A.. (2024). A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:18711-18737 Available from https://proceedings.mlr.press/v235/hong24e.html.

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