# Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-assisted Networks

arXiv: https://arxiv.org/abs/1905.02993 · HTML: https://mlsanity.com/paper/1905.02993/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2019.

## Built on by (most cited first, 13 of 13)

- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.08421/) (2024)
- [A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ](https://mlsanity.com/paper/2102.09774/) (2021)
- [UAV Trajectory Planning for AoI-Minimal Data Collection in UAV-Aided IoT Networks by Transformer](https://mlsanity.com/paper/2401.02425/) (2023)
- [Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach](https://mlsanity.com/paper/2309.14757/) (2023)
- [A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks](https://mlsanity.com/paper/2209.09206/) (2022)
- [UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms](https://mlsanity.com/paper/2211.14931/) (2022)
- [Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models](https://mlsanity.com/paper/2401.13827/) (2024)
- [Offline and Distributional Reinforcement Learning for Wireless Communications](https://mlsanity.com/paper/2504.03804/) (2025)
- [AoI-minimizing Scheduling in UAV-relayed IoT Networks](https://mlsanity.com/paper/2107.05181/) (2021)
- [A Practical AoI Scheduler in IoT Networks with Relays](https://mlsanity.com/paper/2203.04227/) (2022)
- [A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning](https://mlsanity.com/paper/2512.21412/) (2025)
- [AUV Trajectory Learning for Underwater Acoustic Energy Transfer and Age Minimization](https://mlsanity.com/paper/2601.08491/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) are expected to be a key component of the next-generation wireless systems. Due to their deployment flexibility, UAVs are being considered as an efficient solution for collecting information data from ground nodes and transmitting it wirelessly to the network. In this paper, a UAV-assisted wireless network is studied, in which energy-constrained ground nodes are deployed to observe different physical processes. In this network, a UAV that has a time constraint for its operation due to its limited battery, moves towards the ground nodes to receive status update packets about their observed processes. The flight trajectory of the UAV and scheduling of status update packets are jointly optimized with the objective of achieving the minimum weighted sum for the age-of-information (AoI) values of different processes at the UAV, referred to as weighted sum-AoI. The problem is modeled as a finite-horizon Markov decision process (MDP) with finite state and action spaces. Since the state space is extremely large, a deep reinforcement learning (RL) algorithm is proposed to obtain the optimal policy that minimizes the weighted sum-AoI, referred to as the age-optimal policy. Several simulation scenarios are considered to showcase the convergence of the proposed deep RL algorithm. Moreover, the results also demonstrate that the proposed deep RL approach can significantly improve the achievable sum-AoI per process compared to the baseline policies, such as the distance-based and random walk policies. The impact of various system design parameters on the optimal achievable sum-AoI per process is also shown through extensive simulations.