论文标题
加速加强学习，以使用连续的课程学习到达
Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning
论文作者
论文摘要
由于依次的决策特征，强化学习在训练机器人行为方面表现出了巨大的希望。但是，所需的大量互动和信息性培训数据为进步提供了主要的绊脚石。在这项研究中，我们专注于加速加强学习（RL）培训并提高多目标达到任务的性能。具体而言，我们提出了一种基于精确的连续课程学习（PCCL）方法，其中在训练过程中逐渐调整了要求，而不是在静态时间表中固定参数。为此，我们探讨了控制培训过程的各种连续课程策略。在模拟和现实世界中，使用通用机器人5E对这种方法进行了测试。实验结果支持以下假设：静态训练时间表是次优的，并且在课程学习中使用适当的衰减功能以更快的方式提供了卓越的结果。
Reinforcement learning has shown great promise in the training of robot behavior due to the sequential decision making characteristics. However, the required enormous amount of interactive and informative training data provides the major stumbling block for progress. In this study, we focus on accelerating reinforcement learning (RL) training and improving the performance of multi-goal reaching tasks. Specifically, we propose a precision-based continuous curriculum learning (PCCL) method in which the requirements are gradually adjusted during the training process, instead of fixing the parameter in a static schedule. To this end, we explore various continuous curriculum strategies for controlling a training process. This approach is tested using a Universal Robot 5e in both simulation and real-world multi-goal reach experiments. Experimental results support the hypothesis that a static training schedule is suboptimal, and using an appropriate decay function for curriculum learning provides superior results in a faster way.
