Analyzing the Shortest Path in a 3D Object Using a Reinforcement Learning Approach
摘要
Finding the shortest path in a 3D mesh is a fundamental problem in various fields, from robotics to fluid simulation. In this article, we compare three popular approaches to solving this problem: the Dijkstra algorithm, the A* algorithm, and reinforcement learning methods (Q-learning and SARSA). We also provide a detailed analysis of the results obtained for each approach.