一种使用 Webots 的无人值守容器化（深度）强化学习架构
An Architecture for Unattended Containerized (Deep) Reinforcement Learning with Webots
AI总结：
针对三维机器人强化学习中仿真与模型开发环境耦合、数据科学家需掌握仿真软件的问题，提出基于 Webots、ROS 与容器的无人值守训练架构并展示 Robotino 实现。
摘要：
随着数据科学应用在各行业中得到采用，工具生态也日趋成熟，以促进此类应用的生命周期，并为相关挑战提供解决方案，从而提升相关人员的生产力。在三维世界中进行智能体强化学习仍可能面临挑战：使用仿真软件所需的知识，以及在无人值守训练流水线中使用独立仿真软件的问题。本文围绕 Robotino 机器人，综述了在三维世界中训练机器人强化学习智能体的工具和方法，并认为面向虚拟世界创建者的仿真环境与面向数据科学家的模型开发环境之间的分离尚未得到充分研究。通常二者是同一环境，数据科学家需要掌握仿真软件知识，才能直接使用其 API。此外，有时虚拟世界创建者和数据科学家甚至会操作相同的文件。我们希望通过描述一种使数据科学家无需了解仿真软件的方法，为该主题作出贡献。我们的方法使用独立仿真软件 Webots、用于与仿真机器人及仿真软件本身通信的 Robot Operating System，以及用于将仿真与模型开发环境分离的容器技术。我们重点关注数据科学家使用的 API，以及独立仿真软件在无人值守训练流水线中的使用。我们展示了与 Robotino 及待学习机器人任务相关的特定部分。
英文摘要：
As data science applications gain adoption across industries, the tooling landscape matures to facilitate the life cycle of such applications and provide solutions to the challenges involved to boost the productivity of the people involved. Reinforcement learning with agents in a 3D world could still face challenges: the knowledge required to use a simulation software as well as the utilization of a standalone simulation software in unattended training pipelines. In this paper we review tools and approaches to train reinforcement learning agents for robots in 3D worlds with respect to the robot Robotino and argue that the separation of the simulation environment for creators of virtual worlds and the model development environment for data scientists is not a well covered topic. Often both are the same and data scientists require knowledge of the simulation software to work directly with their APIs. Moreover, sometimes creators of virtual worlds and data scientists even work on the same files. We want to contribute to that topic by describing an approach where data scientists don't require knowledge about the simulation software. Our approach uses the standalone simulation software Webots, the Robot Operating System to communicate with simulated robots as well as the simulation software itself and container technology to separate the simulation from the model development environment. We put emphasize on the APIs the data scientists work with and the use of a standalone simulation software in unattended training pipelines. We show the parts that are specific to the Robotino and the robot task to learn.
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。
