TOWARDS EFFECTIVE STRATEGIES FOR MOBILE ROBOT USING REINFORCEMENT LEARNING AND GRAPH ALGORITHMS

Abstract. This research paper explores the use of Reinforcement Learning (RL) and traditional graph algorithms like A* for mobile robots in field path planning strategy development. The conducts a comprehensive analysis these by evaluating their performance terms efficiency, scalability, applicability real-world scenarios. results study show that while both RL have benefits limitations, potential to provide more effective scalable solutions applications. also provides ongoing directions aimed at improving concludes offering valuable insights researchers practitioners working robots.
 purpose this project is evaluate algorithms, identify contribute development practical will be robots, as it offer performance.