  As the number of spacecraft on orbit continues to grow, it is challenging for
human operators to constantly monitor and plan for all missions. Autonomous
control methods such as reinforcement learning (RL) have the power to solve
complex tasks while reducing the need for constant operator intervention. By
combining RL solutions with run time assurance (RTA), safety of these systems
can be assured in real time. However, in order to use these algorithms on board
a spacecraft, they must be able to run in real time on space grade processors,
which are typically outdated and less capable than state-of-the-art equipment.
In this paper, multiple RL-trained neural network controllers (NNCs) and RTA
algorithms were tested on commercial-off-the-shelf (COTS) and radiation
tolerant processors. The results show that all NNCs and most RTA algorithms can
compute optimal and safe actions in well under 1 second with room for further
optimization before deploying in the real world.
