A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916270391164928 |
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| author | Hong, Kihyuk Tewari, Ambuj |
| author_facet | Hong, Kihyuk Tewari, Ambuj |
| contents | 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 $O(ε^{-2})$ 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 $O(ε^{-2})$ with partial data coverage assumption. Our work is an improvement upon a recent work that requires $O(ε^{-4})$ samples. Moreover, we extend our algorithm to work in the offline constrained RL setting that enforces constraints on additional reward signals. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_04493 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs Hong, Kihyuk Tewari, Ambuj Machine Learning 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 $O(ε^{-2})$ 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 $O(ε^{-2})$ with partial data coverage assumption. Our work is an improvement upon a recent work that requires $O(ε^{-4})$ samples. Moreover, we extend our algorithm to work in the offline constrained RL setting that enforces constraints on additional reward signals. |
| title | A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.04493 |