# InterQ: A DQN Framework for Optimal Intermittent Control

arXiv: https://arxiv.org/abs/2504.09035 · HTML: https://mlsanity.com/paper/2504.09035/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 12 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 4 of 4)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes](https://mlsanity.com/paper/1910.07072/) (2019)
- [Value of Information in Feedback Control: Quantification](https://mlsanity.com/paper/1812.07534/) (2018)
- [Value of Information in Feedback Control: Global Optimality](https://mlsanity.com/paper/2103.14012/) (2021)

## Built on by (most cited first, 2 of 2)

- [Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance](https://mlsanity.com/paper/2605.12561/) (2026)
- [The Silence that Speaks: Neural Estimation via Communication Gaps](https://mlsanity.com/paper/2512.01056/) (2025)

## Abstract (arXiv)

In this letter, we explore the communication-control co-design of discrete-time stochastic linear systems through reinforcement learning. Specifically, we examine a closed-loop system involving two sequential decision-makers: a scheduler and a controller. The scheduler continuously monitors the system's state but transmits it to the controller intermittently to balance the communication cost and control performance. The controller, in turn, determines the control input based on the intermittently received information. Given the partially nested information structure, we show that the optimal control policy follows a certainty-equivalence form. Subsequently, we analyze the qualitative behavior of the scheduling policy. To develop the optimal scheduling policy, we propose InterQ, a deep reinforcement learning algorithm which uses a deep neural network to approximate the Q-function. Through extensive numerical evaluations, we analyze the scheduling landscape and further compare our approach against two baseline strategies: (a) a multi-period periodic scheduling policy, and (b) an event-triggered policy. The results demonstrate that our proposed method outperforms both baselines. The open source implementation can be found at https://github.com/AC-sh/InterQ.