Integrating Machine Learning for Optimal Path Planning
Shiru Xiao · Journal of Computer Technology and Applied Mathematics · 2025
In the area of AI based path planning, the learner is not told which actions to take, as is common in most forms of machine learning. Instead, the learner must discover through trial and error, which actions yield the most rewards. In the most interesting and challenging cases, actions affect not only the immediate rewards but also the next station or subsequent rewards. The characteristics of trial and error searches and delayed reward are two important distinguishing features of RL, which are defined not by characterizing learning methods, but by characterizing a learning problem.