通过迭代动作掩码学习实现机器人码垛任务规划器的高效强化学习
Efficient Reinforcement Learning of Task Planners for Robotic Palletization through Iterative Action Masking Learning
AI总结：
针对机器人码垛任务规划中动作空间过大的问题，本文提出利用监督学习迭代剪枝和管理动作空间，从而提升强化学习的训练效率、规划有效性与可靠性。
摘要：
开发面向物流场景码垛作业的机器人系统至关重要，可应对供应链管理中对效率与精度的关键需求。本文研究强化学习（RL）在增强此类机器人系统任务规划方面的应用。面对巨大动作空间这一严重阻碍现成RL方法高效应用的重大挑战，本研究提出一种新方法，利用监督学习迭代式地剪枝并有效管理动作空间。通过降低动作空间的复杂度，该方法不仅加快了学习阶段，还确保了机器人码垛中任务规划的有效性与可靠性。实验结果证实了该方法的功效，突显其在提升物流码垛等复杂、高维环境中RL应用性能方面的潜力。
英文摘要：
The development of robotic systems for palletization in logistics scenarios is of paramount importance, addressing critical efficiency and precision demands in supply chain management. This paper investigates the application of Reinforcement Learning (RL) in enhancing task planning for such robotic systems. Confronted with the substantial challenge of a vast action space, which is a significant impediment to efficiently apply out-of-the-shelf RL methods, our study introduces a novel method of utilizing supervised learning to iteratively prune and manage the action space effectively. By reducing the complexity of the action space, our approach not only accelerates the learning phase but also ensures the effectiveness and reliability of the task planning in robotic palletization. The experimental results underscore the efficacy of this method, highlighting its potential in improving the performance of RL applications in complex and high-dimensional environments like logistics palletization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
