mlsanityData to 14 Oct 2026

A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applications

arXiv 2603.04353 ↗First posted 4 Mar 2026cs.NI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Next-generation networks aim to provide performance guarantees to real-time interactive services that require timely and cost-efficient packet delivery. In this context, the goal is to reliably deliver packets with strict deadlines imposed by the application while minimizing overall resource allocation cost. A large body of work has leveraged stochastic optimization techniques to design efficient dynamic routing and scheduling solutions under average delay constraints; however, these methods fall short when faced with strict per-packet delay requirements. We formulate the minimum-cost delay-constrained network control problem as a constrained Markov decision process and utilize constrained deep reinforcement learning (CDRL) techniques to effectively minimize total resource allocation cost while maintaining timely throughput above a target reliability level. Results indicate that the proposed CDRL-based solution can ensure timely packet delivery even when existing baselines fall short, and it achieves lower cost compared to other throughput-maximizing methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 22 references that are in the map. First posted on arXiv 4 Mar 2026.

Builds on

13 of 22 references.

References · 13

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  3. 2020Implementation Matters in Deep Policy Gradients: A Case Study on PPO and TRPOcited by 186
  4. 2019Constrained Reinforcement Learning Has Zero Duality Gapcited by 151
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  12. 2022Ultra-Reliable Distributed Cloud Network Control with End-to-End Latency Constraintscited by 3
1 more
  1. 2018Optimal Control of Distributed Computing Networks with Mixed-Cast Traffic Flowscited by 2

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