Reinforcement Learning in Real World Application: A Study on Robotics; Autonomous Vehicles and Industrial Automation - B. Shyam Praveen (International Journal of Advanced Robotics and Automation Technology, 2024)
This research paper investigates the practical application of reinforcement learning (RL) in three critical domains: robotics, autonomous vehicles, and industrial automation. The study delves into the implementation of RL algorithms to enhance decision-making, adaptability, and autonomy in these real-world scenarios. Through a comprehensive review of existing literature, methodologies, and case studies, the paper addresses the challenges faced and the successes achieved in deploying RL in each domain. The findings offer valuable insights into the potential of RL to revolutionize robotics, autonomous vehicles, and industrial automation, paving the way for increased efficiency, adaptability, and performance in dynamic and complex environments. The paper concludes by highlighting key challenges, proposing future research directions, and emphasizing the significance of ongoing advancements in reinforcement learning for practical, transformative applications in these critical fields.
Keywords: Reinforcement learning (RL); Robotics; autonomous vehicles; future research directions; real-world scenarios