# Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees

arXiv: https://arxiv.org/abs/2507.08653 · HTML: https://mlsanity.com/paper/2507.08653/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Wireless Network Design for Control Systems: A Survey](https://mlsanity.com/paper/1708.07353/) (2017)
- [On The Age Of Information In Status Update Systems With Packet Management](https://mlsanity.com/paper/1506.08637/) (2015)
- [Joint Power and Blocklength Optimization for URLLC in a Factory Automation Scenario](https://mlsanity.com/paper/1911.13050/) (2019)
- [Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach](https://mlsanity.com/paper/2210.00673/) (2022)
- [Optimization Theory Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systems](https://mlsanity.com/paper/2311.16895/) (2023)
- [Fuzzy Q-Learning-Based Opportunistic Communication for MEC-Enhanced Vehicular Crowdsensing](https://mlsanity.com/paper/2405.01057/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning](https://mlsanity.com/paper/2512.21412/) (2025)

## Abstract (arXiv)

In Wireless Networked Control Systems (WNCSs), control and communication systems must be co-designed due to their strong interdependence. This paper presents a novel optimization theory-based safe deep reinforcement learning (DRL) framework for ultra-reliable WNCSs, ensuring constraint satisfaction while optimizing performance, for the first time in the literature. The approach minimizes power consumption under key constraints, including Peak Age of Information (PAoI) violation probability, transmit power, and schedulability in the finite blocklength regime. PAoI violation probability is uniquely derived by combining stochastic maximum allowable transfer interval (MATI) and maximum allowable packet delay (MAD) constraints in a multi-sensor network. The framework consists of two stages: optimization theory and safe DRL. The first stage derives optimality conditions to establish mathematical relationships among variables, simplifying and decomposing the problem. The second stage employs a safe DRL model where a teacher-student framework guides the DRL agent (student). The control mechanism (teacher) evaluates compliance with system constraints and suggests the nearest feasible action when needed. Extensive simulations show that the proposed framework outperforms rule-based and other optimization theory based DRL benchmarks, achieving faster convergence, higher rewards, and greater stability.