Drone path optimization in complex environment based on Q-learning algorithm

Path planning of intelligent agents in an emergency context is one of the most popular issues within nowadays context. This work proposes an environment acquisition and a path optimization solution based on reinforcement learning. The proposed solution implements Q-Learning algorithm and enables the agent to choose the path that maximizes the reward and minimizes the penalty. When tested in an experiment grid and compared to other solutions the proposed solution proved to be more stable and more efficient.