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

📰 ArXiv cs.AI

Interval-aware reinforcement learning learns optimal action timing using predictive temporal structure

advanced Published 25 Mar 2026

Action Steps

Learn the optimal interval between cognitive ticks from experience
Use a predictive hyperbolic spread signal to augment policy state
Replace ad hoc timers with a principled learned policy
Evaluate the performance of the interval-aware reinforcement learning system

Who Needs to Know This

AI engineers and ML researchers benefit from this research as it improves autonomous agent decision-making in continuous environments, enabling more efficient and effective action timing

Key Insight

💡 Learning the optimal interval between actions improves autonomous agent performance in continuous environments

Key Takeaways

Interval-aware reinforcement learning learns optimal action timing using predictive temporal structure

Full Article

Title: Learning When to Act: Interval-Aware Reinforcement Learning with Predictive Temporal Structure

Abstract:
arXiv:2603.22384v1 Announce Type: cross 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 g

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