Intermittently resetting an agent to a fixed state significantly accelerates policy convergence in Reinforcement Learning.
March 18, 2026
Original Paper
Stochastic Resetting Accelerates Policy Convergence in Reinforcement Learning
arXiv · 2603.16842
The Takeaway
By translating 'stochastic resetting' from statistical mechanics to RL, the authors provide a simple, tunable mechanism to improve exploration in sparse-reward environments. It preserves the optimal policy while truncating uninformative trajectories that typically slow down value propagation.
From the abstract
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. In tabular grid environments, we find that resetting accelerates policy convergence even when it does not reduce the search t