Reinforcement Learning Path Planning for Cleaning Robots

A new research paper on arXiv proposes a reinforcement learning approach for cleaning robot path planning that works across various environments without retraining. The study addresses the increasing household electricity consumption due to the rising demand for cleaning robots. While previous RL models operate only in specific cleaning environments, this method combines the proximal policy optimization (PPO) algorithm with efficient path planning, using transfer learning (TL) and detection of the nearest cleaned tile. The approach aims to enable robots to clean entire spaces efficiently, not just simple path segments. The paper, identified as arXiv:2208.08211v2, was announced as a replace-cross type. The research highlights the integration of deep learning techniques to improve the adaptability and efficiency of cleaning robots, potentially reducing energy consumption in households.