As technology progresses, industrial and scientific robots are increasingly being used in diverse settings. In many cases, however, programming the robot to perform such tasks is technically complex and costly. To maximize the utility of robots in industrial and scientific settings, they require the ability to quickly shift from one task to another. Reinforcement learning algorithms provide the potential to enable robots to learn optimal solutions to complete new tasks without directly reprogramming them. The current state-of-the-art in reinforcement learning, however, generally relies on fast simulations and parallelization to achieve optimal performance. These are often not possible in robotics applications. Thus, a significant amount of research is required to facilitate the efficient and safe, training and deployment of industrial and scientific reinforcement learning robots. This technical report outlines our initial research into the application of deep reinforcement learning on an industrial UR10e robot. The report describes the reinforcement learning environments created to facilitate policy learning with the UR10e, a robotic arm from Universal Robots, and presents our initial results in training deep Q-learning and proximal policy optimization agents on the developed reinforcement learning environments. Our results show that proximal policy optimization learns a better, more stable policy with less data than deep Q-learning. The corresponding code for this work is available at \url{https://github.com/cbellinger27/bendRL_reacher_tracker}

翻译：随着技术进步，工业和科学机器人正越来越多地被应用于各种场景。然而，在许多情况下，为机器人编程以执行此类任务在技术上复杂且成本高昂。为了最大化机器人在工业和科学环境中的效用，它们需要具备快速切换任务的能力。强化学习算法提供了使机器人学会完成新任务的最优解决方案而无需直接重新编程的可能性。然而，当前强化学习的最新技术水平通常依赖于快速仿真和并行化来实现最优性能，这在机器人应用中往往难以实现。因此，需要大量研究来促进工业及科学强化学习机器人的高效、安全训练与部署。本技术报告概述了我们在工业UR10e机器人上应用深度强化学习的初步研究。报告描述了为促进优傲机器人公司生产的UR10e机械臂策略学习而创建的强化学习环境，并展示了我们在所开发的强化学习环境中训练深度Q学习与近端策略优化智能体的初步结果。结果表明，与深度Q学习相比，近端策略优化能以更少的数据学习到更优、更稳定的策略。本工作相关代码见\url{https://github.com/cbellinger27/bendRL_reacher_tracker}。