  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}
