改进Q-learning算法，让机器人更高效完成清洁任务。
Accelerated Multi-objective Task Learning using Modified Q-learning Algorithm
- 引入距离度量缩放机制，优化智能体决策路径。
- 在3×3和4×4网格中成功率分别达86%和59%。
- 相比传统方法，移动距离减少6.7%至8.61%，适合机器人路径规划场景。
机器人在工业和家庭场景中应用广泛。本文提出一种改进的Q-learning算法——带缩放距离度量的Q-learning（Q-SD），旨在提升任务学习效率并使任务完成更具意义。将该算法应用于机械臂清洁桌面的任务中，通过网格化划分桌面区域（3×3和4×4两种尺寸），智能体利用Q-SD算法学习最优操作序列，同时最小化机械臂移动距离。实验结果表明，在3×3和4×4网格环境中，最大成功率达到86%和59%。相较于传统Q-learning，Q-SD在两个环境中的平均移动距离分别减少了8.61%和6.7%。
原文摘要 · Abstract (English)
Robots find extensive applications in industry. In recent years, the influence of robots has also increased rapidly in domestic scenarios. The Q-learning algorithm aims to maximise the reward for reaching the goal. This paper proposes a modified version of the Q-learning algorithm, known as Q-learning with scaled distance metric (Q-SD). This algorithm enhances task learning and makes task completion more meaningful. A robotic manipulator (agent) applies the Q-SD algorithm to the task of table cleaning. Using Q-SD, the agent acquires the sequence of steps necessary to accomplish the task while minimising the manipulator's movement distance. We partition the table into grids of different dimensions. The first has a grid count of 3 times 3, and the second has a grid count of 4 times 4. Using the Q-SD algorithm, the maximum success obtained in these two environments was 86% and 59% respectively. Moreover, Compared to the conventional Q-learning algorithm, the drop in average distance moved by the agent in these two environments using the Q-SD algorithm was 8.61% and 6.7% respectively.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。