Stochastic Resetting Accelerates Reinforcement Learning Beyond Random Search
Stochastic resetting -- intermittently returning a process to a fixed reference state -- has emerged as an effective mechanism for optimizing first-passage properties. Existing theory largely treats processes that search but do not learn: the searcher follows fixed dynamics, accumulating no knowledge between resets. Here we ask how stochastic resetting interacts with reinforcement learning, where the underlying dynamics adapt through experience. In tabular grid environments, we find that resetting can accelerate learning even when it does not reduce the search time of a diffusive agent. Our results reveal a distinct additional mechanism through which resetting speeds the propagation of re...
| Authors | Jello Zhou, David J. Schwab, Vudtiwat Ngampruetikorn |
|---|---|
| Primary category | cs.LG |
| Published | 17 March 2026 |
| Last updated at source | 20 July 2026 |
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