The Sample Complexity of Policy Learning with Mu-Resets
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Computer Science > Machine Learning
Title:The Sample Complexity of Policy Learning with Mu-Resets
Abstract:We study policy-based reinforcement learning under the $\mu$-resets interaction protocol of Kakade and Langford [KL02]. This interaction protocol enables the learner to sample trajectories from a given exploratory reset distribution $\mu$, in addition to the starting distribution. We resolve the question raised by [KLS25] on the role of policy realizability for the sample complexity of this problem. Critically, the dependence on horizon $H$ is governed by the notion of coverage assumed of the reset distribution. Under bounded all-policy concentrability, we show a $\exp(\Omega(H))$ sample complexity lower bound; with bounded pushforward concentrability, we show the dependence on horizon is tightly characterized as $\exp(\Theta(\sqrt H))$.
| Comments: | comments welcome |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.07772 [cs.LG] |
| (or arXiv:2608.07772v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.07772
arXiv-issued DOI via DataCite (pending registration)
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