# Optimizing Search and Rescue UAV Connectivity in Challenging Terrain through Multi Q-Learning

arXiv: https://arxiv.org/abs/2405.10042 · HTML: https://mlsanity.com/paper/2405.10042/

## Key facts

- Cited by 2 papers in the map (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 3 of its 19 references that are in the map.
- First posted on arXiv 16 May 2024.

## 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, 3 of 3)

- [Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search](https://mlsanity.com/paper/1902.10182/) (2019)
- [Machine Learning-based xApp for Dynamic Resource Allocation in O-RAN Networks](https://mlsanity.com/paper/2401.07643/) (2024)
- [FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA](https://mlsanity.com/paper/2304.13549/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives](https://mlsanity.com/paper/2503.13415/) (2025)
- [Semantic-Aware Edge Intelligence for UAV Handover in 6G Networks](https://mlsanity.com/paper/2509.22668/) (2025)

## Abstract (arXiv)

Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique employing a reinforcement learning multi Q-learning algorithm to optimize UAV connectivity in such scenarios. We introduce a Strategic Planning Agent for efficient path planning and collision awareness and a Real-time Adaptive Agent to maintain optimal connection with the cellular base station. The agents trained in a simulated environment using multi Q-learning, encouraging them to learn from experience and adjust their decision-making to diverse terrain complexities and communication scenarios. Evaluation results reveal the significance of the approach, highlighting successful navigation in environments with varying obstacle densities and the ability to perform optimal connectivity using different frequency bands. This work paves the way for enhanced UAV autonomy and enhanced communication reliability in search and rescue operations.