  There is an increasing demand for using Unmanned Aerial Vehicle (UAV), known
as drones, in different applications such as packages delivery, traffic
monitoring, search and rescue operations, and military combat engagements. In
all of these applications, the UAV is used to navigate the environment
autonomously - without human interaction, perform specific tasks and avoid
obstacles. Autonomous UAV navigation is commonly accomplished using
Reinforcement Learning (RL), where agents act as experts in a domain to
navigate the environment while avoiding obstacles. Understanding the navigation
environment and algorithmic limitations plays an essential role in choosing the
appropriate RL algorithm to solve the navigation problem effectively.
Consequently, this study first identifies the main UAV navigation tasks and
discusses navigation frameworks and simulation software. Next, RL algorithms
are classified and discussed based on the environment, algorithm
characteristics, abilities, and applications in different UAV navigation
problems, which will help the practitioners and researchers select the
appropriate RL algorithms for their UAV navigation use cases. Moreover,
identified gaps and opportunities will drive UAV navigation research.
