Evaluation of the Deep Q-Learning Models for Mobile Robot Path Planning Problem

Search algorithms such as A* or Dijkstra are generally used to solve the path planning problem for mobile robots. However, these approaches require a map and their performance decreases in dynamic environments. These drawbacks have led researchers to work on dynamic path planning algorithms. Deep reinforcement learning methods have been extensively studied for this purpose and their use is expanding day by day. However, these studies mostly focus on training performance of the models, but not on inference. In this study, we propose an approach to compare the performance of the models in terms of path length, path curvature and journey time. We implemented the approach by using Python programming language two steps: inference and evaluation. Inference step gathers information of path planning performance; evaluation step computes the metrics regarding the information. Our approach can be tailored to many studies to examine the performances of trained models.