C-Learning: Horizon-Aware Cumulative Accessibility Estimation

We introduce cumulative accessibility functions, which measure the reachability of a goal from a given state within a specified horizon. We show that optimal cumulative accessibility functions are monotonic and can trade off speed and reliability in goal-reaching by suggesting multiple paths to a single goal depending on the provided horizon. We show that our method outperforms state-of-the-art goal-reaching algorithms in success rate, sample complexity, and path optimality.