OMB Individually Reported
Reinforcement Learning for Lunar Lander Real Time Optimal Trajectory
Low riskExact public inventory row
Description
The proposal's objective is to apply RL for real-time optimal guidance and control in spacecraft systems, aiming to enhance efficiency, robustness, and autonomy.
Detailed example
real-time optimal guidance and control in spacecraft systems
AI / analytics pattern
Reinforcement Learning: AI trained through trial and error using rewards and penalties to optimize decision-making policies.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
The proposal's objective is to apply RL for real-time optimal guidance and control in spacecraft systems, aiming to enhance efficiency, robustness, and autonomy.
Controls / human review
ATO: Not reported; PIA: Not published