# Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning

arXiv: https://arxiv.org/abs/2601.14092 · HTML: https://mlsanity.com/paper/2601.14092/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 20 of its 43 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Challenges of Real-World Reinforcement Learning](https://mlsanity.com/paper/1904.12901/) (2019)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Soft Actor-Critic for Discrete Action Settings](https://mlsanity.com/paper/1910.07207/) (2019)
- [A Survey of Zero-shot Generalisation in Deep Reinforcement Learning](https://mlsanity.com/paper/2111.09794/) (2021)
- [Dynamic Weights in Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1809.07803/) (2018)
- [Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework](https://mlsanity.com/paper/2311.12495/) (2023)
- [Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks](https://mlsanity.com/paper/2012.03158/) (2020)
- [Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design](https://mlsanity.com/paper/1904.05242/) (2019)
- [Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12461/) (2020)
- [Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT Networks](https://mlsanity.com/paper/2003.00391/) (2020)
- [UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2007.00544/) (2020)
- [3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning](https://mlsanity.com/paper/2106.03129/) (2021)
- [Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2107.11015/) (2021)
- [UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning](https://mlsanity.com/paper/2010.06917/) (2020)
- [An Overview of Drone Energy Consumption Factors and Models](https://mlsanity.com/paper/2206.10775/) (2022)
- [Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks](https://mlsanity.com/paper/2306.02029/) (2023)

## Abstract (arXiv)

Due to their adaptability and mobility, Unmanned Aerial Vehicles (UAVs) are becoming increasingly essential for wireless network services, particularly for data harvesting tasks. In this context, Artificial Intelligence (AI)-based approaches have gained significant attention for addressing UAV path planning tasks in large and complex environments, bridging the gap with real-world deployments. However, many existing algorithms suffer from limited training data, which hampers their performance in highly dynamic environments. Moreover, they often overlook the inherently multi-objective nature of the task, treating it in an overly simplistic manner. To address these limitations, we propose an attention-based Multi-Objective Reinforcement Learning (MORL) architecture that explicitly handles the trade-off between data collection and energy consumption in urban environments, even without prior knowledge of wireless channel conditions. Our method develops a single model capable of adapting to varying trade-off preferences and dynamic scenario parameters without the need for fine-tuning or retraining. Extensive simulations show that our approach achieves substantial improvements in performance, model compactness, sample efficiency, and most importantly, generalization to previously unseen scenarios, outperforming existing RL solutions.