# Learning Multi-Timescale Interventions under Safety and Resource Constraints

arXiv: https://arxiv.org/abs/2508.03875 · HTML: https://mlsanity.com/paper/2508.03875/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark](https://mlsanity.com/paper/2310.12567/) (2023)
- [OR-Gym: A Reinforcement Learning Library for Operations Research Problems](https://mlsanity.com/paper/2008.06319/) (2020)
- [Safe Offline Reinforcement Learning with Real-Time Budget Constraints](https://mlsanity.com/paper/2306.00603/) (2023)
- [Flexible Option Learning](https://mlsanity.com/paper/2112.03097/) (2021)
- [Dynamic Decision Frequency with Continuous Options](https://mlsanity.com/paper/2212.04407/) (2022)
- [Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays](https://mlsanity.com/paper/2402.03141/) (2024)
- [Structure-Informed Deep Reinforcement Learning for Inventory Management](https://mlsanity.com/paper/2507.22040/) (2025)
- [MARLIM: Multi-Agent Reinforcement Learning for Inventory Management](https://mlsanity.com/paper/2308.01649/) (2023)
- [Reconciling Spatial and Temporal Abstractions for Goal Representation](https://mlsanity.com/paper/2401.09870/) (2024)
- [Learning Uncertainty-Aware Temporally-Extended Actions](https://mlsanity.com/paper/2402.05439/) (2024)
- [Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints](https://mlsanity.com/paper/2205.15953/) (2022)
- [Simultaneously Updating All Persistence Values in Reinforcement Learning](https://mlsanity.com/paper/2211.11620/) (2022)
- [When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions](https://mlsanity.com/paper/2406.07897/) (2024)

## Abstract (arXiv)

Many sequential decision problems offer qualitatively different ways of influencing the environment: some interventions act immediately, whereas others induce persistent effects that continue to shape future states long after the decision that initiated them. An agent must then decide jointly when to intervene, which temporal mode to use and how strongly, while accounting for residual effects and limited intervention resources. We introduce MINT: Multi-timescale Intervention Network Training. Persistent effects are carried by an augmented intervention state that accumulates and decays, while a structured policy separates intervention-mode selection from conditional control. Unlike temporal abstractions that extend policy execution, persistent-effect interventions remain part of the environment dynamics and may overlap with later interventions. We show that the augmented state is a sufficient statistic for the intervention history, preserving the Markov property, and establish Bellman contraction and almost-sure convergence of a tabular Q-learning instance. Across persistent-control MuJoCo locomotion, stochastic inventory management, and a physiologically grounded Type 1 Diabetes Mellitus (T1DM) simulator, MINT attains the best mean primary metric on two of three benchmarks while using fewer intervention activations than an identically augmented flat policy. Its return advantage is present at every binding intervention budget and narrows to parity in the unconstrained reference setting. In T1DM it achieves 90.9±0.9% time in range with zero time below range, improving on the strongest baseline by 21.5% points. Code, benchmarks and the configuration for every reported run are available at https://github.com/heywanrong/mint-rl.