Stochastic Resetting Accelerates Policy Convergence in Reinforcement Learning

ArXi:2603.16842v1 Announce Type: new Stochastic resetting, where a dynamical process is intermittently returned to a fixed reference state, has emerged as a powerful mechanism for optimizing first-passage properties. Existing theory largely treats static, non-learning processes. Here we ask how stochastic resetting interacts with reinforcement learning, where the underlying dynamics adapt through experience.