Reinforcement learning for reachability—steering an agent to a target set of states—is a fundamental problem in sequential decision-making. The abstract of a new arXiv paper (2610.01781) notes that prior work has established asymptotic convergence to optimal policies, but only through model-based methods that must explicitly [the abstract text cuts off here].
The paper's title points to the proposed alternative: Q-learning for reachability in MEC-free MDPs. This suggests a model-free approach, in contrast to the model-based methods that previously carried convergence guarantees. The available abstract does not elaborate on the algorithm or the precise conditions, so the details of the contribution are not described in the source.
Because the abstract is truncated, this digest can only report what is visible. Readers interested in the full method and results will need to consult the paper directly.