Deep Reinforcement Learning for Autonomous Indoor Exploration with UAVs

This master’s thesis addresses the challenge of autonomous exploration in unknown, office-like indoor environments using an Unmanned Aerial Vehicle (UAV). The primary objective is to train a UAV to explore such environments through Deep Reinforcement Learning (DRL) techniques within a physics-accurate, photorealistic simulation framework.