Area Scanning with Reinforcement Learning and MCTS in Smart City Applications

Abstract

This research project is focused on area scanning in  the scope of smart city applications with unmanned aerial vehicles or UAVs. More powerful devices have been designed in terms of range, capacity and sensory capabilities in the recent years. This makes possible easier automation, thus suppressing the need for human resources. Some of the fields of applications include: traffic or pollution monitoring, land surveying, civil security control or natural disaster control and monitoring. With the increased number of UAV applications, the use and development of efficient algorithms is more and more essential. This paper investigates the possibility of using Monte-Carlo Tree Search (MCTS) and Reinforcement Learning (RL) in this area, which are already successful methods in other control tasks.