[2603.22384] Learning When to Act: Interval-Aware Reinforcement Learning with Predictive Temporal Structure

Computer Science > Machine Learning arXiv:2603.22384 (cs) [Submitted on 23 Mar 2026] Title:Learning When to Act: Interval-Aware Reinforcement Learning with Predictive Temporal Structure Authors:Davide Di Gioia View a PDF of the paper titled Learning When to Act: Interval-Aware Reinforcement Learning with Predictive Temporal Structure, by Davide Di Gioia View PDF HTML (experimental) Abstract:Autonomous agents operating in continuous environments must decide not only what to do, but when to act. We introduce a lightweight adaptive temporal control system that learns the optimal interval between cognitive ticks from experience, replacing ad hoc biologically inspired timers with a principled learned policy. The policy state is augmented with a predictive hyperbolic spread signal (a "curvature signal" shorthand) derived from hyperbolic geometry: the mean pairwise Poincare distance among n sampled futures embedded in the Poincare ball. High spread indicates a branching, uncertain future and drives the agent to act sooner; low spread signals predictability and permits longer rest intervals. We further propose an interval-aware reward that explicitly penalises inefficiency relative to the chosen wait time, correcting a systematic credit-assignment failure of naive outcome-based rewards in timing problems. We additionally introduce a joint spatio-temporal embedding (ATCPG-ST) that concatenates independently normalised state and position projections in the Poincare ball; spatial tra...