Reinforcement Learning Based Path Planning for Autonomous Flight

Path planning can be considered one of the oldest problems that people have tried to solve since the early days of civilization. Many mathematicians and scientists have looked into this problem, and it has many applications in science, engineering, economics, logistics, and many other fields. Path planning is now an active topic of research, with many advancements made over the years to produce better and faster algorithms. Because of recent developments in areas such as machine vision, machine learning, and robotics, path planning is now part of even more applications. This is because path planning can be used to help robots and autonomous systems find their way around in environments they don’t know much about. In order for an autonomous system to learn to complete a given task in an uncertain, potentially complex environment, reinforcement learning can be used to train neural network models to make a series of decisions in order to select the action to be performed based on the current state of the environment without having an accurate model of it.