Enhancing Autonomous Robotic Navigation Through Reinforcement Learning

Abstract

The integration of reinforcement learning in autonomous robotic navigation has shown significant promise in improving the adaptability and efficiency of robots in dynamic environments. This study explores the application of reinforcement learning algorithms to enhance the decision-making processes of autonomous robots. By training robots in simulated environments and applying learned strategies in real-world scenarios, we demonstrate the potential for improved obstacle avoidance, path planning, and environmental interaction. Our findings indicate that reinforcement learning can substantially elevate the performance and reliability of autonomous robotic systems.