  Unmanned Aerial Vehicles need an online path planning capability to move in
high-risk missions in unknown and complex environments to complete them safely.
However, many algorithms reported in the literature may not return reliable
trajectories to solve online problems in these scenarios. The Q-Learning
algorithm, a Reinforcement Learning Technique, can generate trajectories in
real-time and has demonstrated fast and reliable results. This technique,
however, has the disadvantage of defining the iteration number. If this value
is not well defined, it will take a long time or not return an optimal
trajectory. Therefore, we propose a method to dynamically choose the number of
iterations to obtain the best performance of Q-Learning. The proposed method is
compared to the Q-Learning algorithm with a fixed number of iterations, A*,
Rapid-Exploring Random Tree, and Particle Swarm Optimization. As a result, the
proposed Q-learning algorithm demonstrates the efficacy and reliability of
online path planning with a dynamic number of iterations to carry out online
missions in unknown and complex environments.
