# Using Machine Learning to Take Stay-or-Go Decisions in Data-driven Drone Missions

arXiv: https://arxiv.org/abs/2512.04773 · HTML: https://mlsanity.com/paper/2512.04773/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 4 Dec 2025.

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

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [A Comparative Study of Deep Reinforcement Learning Models: DQN vs PPO vs A2C](https://mlsanity.com/paper/2407.14151/) (2024)
- [A Survey of Techniques for Dynamic Branch Prediction](https://mlsanity.com/paper/1804.00261/) (2018)
- [Path Planning for Cellular-Connected UAV: A DRL Solution with Quantum-Inspired Experience Replay](https://mlsanity.com/paper/2108.13184/) (2021)
- [A Survey of Deep Learning Techniques for Dynamic Branch Prediction](https://mlsanity.com/paper/2112.14911/) (2021)
- [A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches](https://mlsanity.com/paper/2204.07967/) (2022)
- [UAV Trajectory Optimization for Directional THz Links Using Deep Reinforcement Learning](https://mlsanity.com/paper/2307.05535/) (2023)
- [Flexible Computation Offloading at the Edge for Autonomous Drones with Uncertain Flight Times](https://mlsanity.com/paper/2310.11895/) (2023)
- [Should I Stay or Should I Go: A Learning Approach for Drone-based Sensing Applications](https://mlsanity.com/paper/2409.04764/) (2024)

## Abstract (arXiv)

Drones are becoming indispensable in many application domains. In data-driven missions, besides sensing, the drone must process the collected data at runtime to decide whether additional action must be taken on the spot, before moving to the next point of interest. If processing does not reveal an event or situation that requires such an action, the drone has waited in vain instead of moving to the next point. If, however, the drone starts moving to the next point and it turns out that a follow-up action is needed at the previous point, it must spend time to fly-back. To take this decision, we propose different machine-learning methods based on branch prediction and reinforcement learning. We evaluate these methods for a wide range of scenarios where the probability of event occurrence changes with time. Our results show that the proposed methods consistently outperform the regression-based method proposed in the literature and can significantly improve the worst-case mission time by up to 4.1x. Also, the achieved median mission time is very close, merely up to 2.7% higher, to that of a method with perfect knowledge of the current underlying event probability at each point of interest.