PI: Dr. Tarek Sheltami
CoI: Dr. Ashraf Mahmoud

The increasing reliance on Unmanned Aerial Vehicles (UAVs) for radio environment mapping (REM) has highlighted the need for adaptive and accurate trajectory planning for these UAVs. We suggest the potential of Reinforcement Learning (RL) to optimize UAV trajectory planning for enhanced REM creation. In situations such as disaster recovery or emergency response, where speed and real-time decision-making are critical, RL enables UAVs to optimize their flight paths, to ensure high-quality REM creation.