Sample-efficiency in multi-batch reinforcement learning: the need for dimension-dependent adaptivity
We theoretically explore the relationship between sample-efficiency and adaptivity in reinforcement learning. An algorithm is sample-efficient if it uses a number of queries n to the environment that is polynomial in the dimension d of the problem. Adaptivity refers to the frequency at which queries...
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| Médium: | Conference item |
| Jazyk: | English |
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OpenReview
2024
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