mlsanityData to 14 Oct 2026

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

arXiv 2405.06771 ↗First posted 10 May 2024eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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algorithms on board a spacecraft, they must be able to run in real time on space grade processors, which are typically outdated and less capable than state-of-the-art equipment.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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 20 of its 48 references that are in the map. First posted on arXiv 10 May 2024.

Builds on

20 of 48 references.

References · 20

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  11. 2021Run Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systemscited by 27
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8 more
  1. 2018Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehiclescited by 19
  2. 2020Learning Safe Neural Network Controllers with Barrier Certificatescited by 18
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  5. 2023Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illuminationcited by 8
  6. 2022Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agentscited by 5
  7. 2023Run Time Assurance for Autonomous Spacecraft Inspectioncited by 5
  8. 2022A Universal Framework for Generalized Run Time Assurance with JAX Automatic Differentiationcited by 3

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  2. 2024Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection
  3. 2025Toward Trusted Onboard AI: Advancing Small Satellite Operations using Reinforcement Learning
  4. 2025RL-AVIST: Reinforcement Learning for Autonomous Visual Inspection of Space Targets
  5. 2025Crossing the Sim2Real Gap Between Simulation and Ground Testing to Space Deployment of Autonomous Free-flyer Control
  6. 2024Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection
  7. 2026Deep Reinforcement Learning for Spacecraft Attitude Control During Atmospheric Re-Entry
  8. 2025OrbitZoo: Real Orbital Systems Challenges for Reinforcement Learning