论文标题
基于好奇心的强化学习机器人制造单元格
Curiosity Based Reinforcement Learning on Robot Manufacturing Cell
论文作者
论文摘要
本文介绍了针对灵活的机器人制造单元格和基于好奇心的增强学习的新型调度控制。事实证明，强化学习在解决机器人技术和计划等任务方面非常成功。但这需要在诸如机器人技术之类的问题域和调度方面的奖励进行手动调整，即使解决方案并不明显。为此，我们将基于好奇心的增强学习（使用内在动机作为奖励形式）应用于灵活的机器人制造细胞上，以减轻此问题。此外，学习代理被嵌入到运输机器人中，以实现可以应用于各种环境的广义学习解决方案。在第一种方法中，基于好奇心的增强学习应用于简单的结构化机器人制造单元。在第二种方法中，将相同的算法应用于图形结构化机器人制造单元格。实验的结果表明，代理能够解决两个环境，并能够将好奇心模块直接从一个环境传输到另一个环境。我们得出的结论是，基于好奇心的学习对计划任务的学习为传统上使用的奖励形状增强学习提供了可行的替代方法。
This paper introduces a novel combination of scheduling control on a flexible robot manufacturing cell with curiosity based reinforcement learning. Reinforcement learning has proved to be highly successful in solving tasks like robotics and scheduling. But this requires hand tuning of rewards in problem domains like robotics and scheduling even where the solution is not obvious. To this end, we apply a curiosity based reinforcement learning, using intrinsic motivation as a form of reward, on a flexible robot manufacturing cell to alleviate this problem. Further, the learning agents are embedded into the transportation robots to enable a generalized learning solution that can be applied to a variety of environments. In the first approach, the curiosity based reinforcement learning is applied to a simple structured robot manufacturing cell. And in the second approach, the same algorithm is applied to a graph structured robot manufacturing cell. Results from the experiments show that the agents are able to solve both the environments with the ability to transfer the curiosity module directly from one environment to another. We conclude that curiosity based learning on scheduling tasks provide a viable alternative to the reward shaped reinforcement learning traditionally used.
