# Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Recharge

arXiv: https://arxiv.org/abs/2301.03423 · HTML: https://mlsanity.com/paper/2301.03423/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2023.

## Built on by (most cited first, 9 of 9)

- [Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2402.08421/) (2024)
- [Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach](https://mlsanity.com/paper/2309.14757/) (2023)
- [An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management](https://mlsanity.com/paper/2501.12991/) (2025)
- [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 Radio Resource Management](https://mlsanity.com/paper/2409.16764/) (2024)
- [Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks](https://mlsanity.com/paper/2501.14603/) (2025)
- [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)
- [Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks](https://mlsanity.com/paper/2606.16331/) (2026)

## Abstract (arXiv)

In many emerging Internet of Things (IoT) applications, the freshness of the is an important design criterion. Age of Information (AoI) quantifies the freshness of the received information or status update. This work considers a setup of deployed IoT devices in an IoT network; multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption. The solution accounts for the UAVs' battery lifetime and flight time to recharging depots to ensure the UAVs' green operation. The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower ergodic age and ergodic energy consumption when compared with benchmark algorithms such as greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).