  The trial and error approach of reinforcement learning (RL) results in high
performance across many complex tasks, but it can also lead to unsafe behavior.
Run time assurance (RTA) approaches can be used to assure safety of the agent
during training, allowing it to safely explore the environment. This paper
investigates the application of RTA during RL training for a
6-Degree-of-Freedom spacecraft inspection task, where the agent must control
its translational motion and attitude to inspect a passive chief spacecraft.
Several safety constraints are developed based on position, velocity, attitude,
temperature, and power of the spacecraft, and are all enforced simultaneously
during training through the use of control barrier functions. This paper also
explores simulating the RL agent and RTA at different frequencies to best
balance training performance and safety assurance. The agent is trained with
and without RTA, and the performance is compared across several metrics
including inspection percentage and fuel usage.
