Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies

TL;DR

This paper evaluates the real-time computation capabilities of reinforcement learning controllers and run time assurance algorithms on space-grade processors, demonstrating their potential for autonomous spacecraft operations.

Contribution

It provides an analysis of neural network controllers and safety algorithms' runtime performance on COTS and radiation-tolerant processors for space applications.

Findings

All neural network controllers compute actions under 1 second.

Most RTA algorithms also run within 1 second.

Results support deployment of RL and RTA in real-time space systems.

Abstract

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algorithms on board a spacecraft, they must be able to run in real time on space grade processors, which are typically outdated and less capable than state-of-the-art equipment.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.