  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.
