Learning When to Act: Interval-Aware Reinforcement Learning with Predictive Temporal Structure
arXiv:2603.22384v1 Announce Type: new 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
arXiv:2603.22384v1 Announce Type: new 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 trajectory divergence provides an independent timing signal unavailable to the state-only variant (ATCPG-SO). This extension raises mean hyperbolic spread (kappa) from 1.88 to 3.37 and yields a further 5.8 percent efficiency gain over the state-only baseline. Ablation experiments across five random seeds demonstrate that (i) learning is the dominant efficiency factor (54.8 percent over no-learning), (ii) hyperbolic spread provides significant complementary gain (26.2 percent over geometry-free control), (iii) the combined system achieves 22.8 percent efficiency over the fixed-interval baseline, and (iv) adding spatial position information to the spread embedding yields an additional 5.8 percent.
Executive Summary
This article presents a novel approach to interval-aware reinforcement learning, introducing a predictive temporal control system that learns the optimal interval between cognitive ticks from experience. The system incorporates a hyperbolic spread signal derived from hyperbolic geometry, which indicates the branching and uncertainty of future outcomes. The authors propose an interval-aware reward to correct systematic credit-assignment failures in timing problems and develop a joint spatio-temporal embedding that combines state and position projections. The approach demonstrates significant efficiency gains over traditional methods, with a 22.8 percent efficiency increase over the fixed-interval baseline. While the article makes valuable contributions to reinforcement learning, its practical applications may be limited to specific domains where temporal control is critical.
Key Points
- ▸ Interval-aware reinforcement learning with predictive temporal structure
- ▸ Hyperbolic spread signal for uncertainty prediction
- ▸ Interval-aware reward for correcting credit-assignment failures
Merits
Strength in Novelty
The article introduces a new approach to interval-aware reinforcement learning, expanding the scope of temporal control in autonomous agents.
Strength in Efficiency
The proposed system demonstrates significant efficiency gains over traditional methods, with a 22.8 percent increase over the fixed-interval baseline.
Demerits
Limitation in Practicality
The article's focus on novel methods and theoretical contributions may limit its practical applications to specific domains where temporal control is critical.
Limitation in Generalizability
The article's results may not generalize to all scenarios, as the performance of the proposed system relies on the specific characteristics of the environments and tasks considered.
Expert Commentary
The article presents a novel and promising approach to interval-aware reinforcement learning, leveraging hyperbolic geometry to predict uncertainty and optimize decision-making. While the results are impressive, the article's practical applications may be limited to specific domains. Further research is needed to explore the generalizability of the proposed system and its potential applications in real-world scenarios.
Recommendations
- ✓ Future research should focus on exploring the generalizability of the proposed system and its applications in diverse domains.
- ✓ The authors should investigate the use of other geometric structures, such as Riemannian manifolds, to further extend the scope of interval-aware reinforcement learning.
Sources
Original: arXiv - cs.LG
