Push and Pull Robot with Reinforcement Learning Algorithms

The objective of this research is to study how basic reinforcement learning (RL) techniques can be applied to dynamic push and pull tasks for a mobile robot. Our goal is to determine the ideal combination of rewards and punishments to improve the learning process by utilizing fundamental concepts of reinforcement learning and closely monitoring the robot’s interactions with its dynamic environment. The research entails creating algorithms specifically designed to handle the special difficulties presented by push and pull jobs, and then rigorously testing and simulating these algorithms. We hope to identify trends and behaviors through this method that will aid in creating an effective and adaptable robotic system. As the research developed, it concentrated on creating algorithms and assessing pre-existing ideas, offering insightful information about the usefulness of reinforcement learning for mobile robots in dynamic environments. The study opens the door for further developments in this area by providing a thorough understanding of RL’s influence on adaptive robotic systems.